{"id":100097,"date":"2026-09-03T22:25:41","date_gmt":"2026-09-03T14:25:41","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/100097.html"},"modified":"2026-09-03T22:25:41","modified_gmt":"2026-09-03T14:25:41","slug":"%e5%9b%be%e5%83%8f%e5%a4%84%e7%90%86%e5%85%a5%e9%97%a8025-%e9%9d%9e%e7%ba%bf%e6%80%a7%e7%81%b0%e5%ba%a6%e5%8f%98%e6%8d%a2%ef%bc%9a%e4%bc%bd%e9%a9%ac%e6%a0%a1%e6%ad%a3%e4%b8%8e-log-%e5%8f%98%e6%8d%a2","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/100097.html","title":{"rendered":"\u56fe\u50cf\u5904\u7406\u5165\u95e8025 | \u975e\u7ebf\u6027\u7070\u5ea6\u53d8\u6362\uff1a\u4f3d\u9a6c\u6821\u6b63\u4e0e Log \u53d8\u6362\u2014\u2014\u6697\u56fe\u6551\u661f\u7684\u4e24\u6761\u66f2\u7ebf"},"content":{"rendered":"

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u7b2c 025 \u671f | \u56fe\u50cf\u5904\u7406\u7b97\u6cd5\u5165\u95e8\u7cfb\u5217 | 2026-09-03
\n\u5173\u952e\u8bcd：\u4f3d\u9a6c\u6821\u6b63\u3001Log \u53d8\u6362\u3001\u975e\u7ebf\u6027\u70b9\u8fd0\u7b97\u3001\u81ea\u52a8\u4f3d\u9a6c\u3001LUT \u52a0\u901f\u3001sRGB \u663e\u793a\u4f3d\u9a6c\u3001\u6697\u56fe\u589e\u5f3a<\/p>\n

\u627f\u63a5 024 \u671f \u2192 025 \u671f：024 \u671f\u6211\u4eec\u7528\u4e00\u6761\u76f4\u7ebf（g = \u03b1\u00b7f + \u03b2）\u641e\u5b9a\u4eae\u5ea6\u4e0e\u5bf9\u6bd4\u5ea6；\u4eca\u665a\u628a\u76f4\u7ebf\u5f2f\u6210\u66f2\u7ebf\u2014\u2014\u4f3d\u9a6c\u4e0e Log\u3002\u5b83\u4eec\u4e13\u6cbb\u4e00\u79cd\u75c5：\u6697\u90e8\u7ec6\u8282\u6324\u5728\u4e00\u8d77，\u7ebf\u6027\u62c9\u4f38\u6551\u4e0d\u56de\u6765\u3002<\/p>\n

\n

\u4e00\u3001\u5f15\u8a00：\u4e3a\u4ec0\u4e48\u9700\u8981"\u66f2\u7ebf"<\/h3>\n

024 \u671f\u7ed3\u5c3e\u7559\u4e86\u4e00\u4e2a\u5c3e\u5df4：\u9762\u5bf9\u4e00\u5f20\u6b20\u66dd\u6697\u56fe，\u7ebf\u6027\u53d8\u6362\u6709\u4e24\u4e2a\u5148\u5929\u7f3a\u9677\u3002<\/p>\n

\u7f3a\u9677 1：\u76f4\u7ebf\u662f"\u4e00\u89c6\u540c\u4ec1"\u7684\u3002
\n g = 1.4\u00b7f + 55 \u786e\u5b9e\u80fd\u63d0\u4eae，\u4f46\u5b83\u5bf9\u6697\u90e8\u3001\u4e2d\u95f4\u8c03\u3001\u9ad8\u5149\u7684\u62c9\u4f38\u6bd4\u4f8b\u5b8c\u5168\u76f8\u540c\u3002<\/p>\n

\u7f3a\u9677 2：\u5bf9\u6bd4\u5ea6\u63d0\u5347（\u7ed5 128 \u62c9\u4f38）\u4f1a\u628a\u6697\u90e8\u538b\u6b7b\u3002
\n g = 1.9\u00b7(f \u2212 128) + 128 \u4f1a\u8ba9\u6240\u6709 f < 61 \u7684\u50cf\u7d20\u76f4\u63a5\u622a\u65ad\u6210 0\u2014\u2014
\n \u4e5f\u5c31\u662f"\u9ed1\u5f97\u4e00\u584c\u7cca\u6d82"，\u6697\u90e8\u7ec6\u8282\u53cd\u800c\u5168\u90e8\u4e22\u5931\u3002<\/p>\n

\u800c\u4eba\u773c\u5bf9\u6697\u90e8\u53d8\u5316\u7684\u654f\u611f\u5ea6\u8fdc\u9ad8\u4e8e\u4eae\u90e8（\u8fd9\u6b63\u662f sRGB \u7f16\u7801\u7684\u8bbe\u8ba1\u521d\u8877）\u3002\u6211\u4eec\u9700\u8981\u4e00\u79cd\u53d8\u6362：\u5728\u4f4e\u7070\u533a\u659c\u7387\u5927（\u628a\u6324\u5728\u4e00\u8d77\u7684\u6697\u90e8\u7ec6\u8282\u62c9\u5f00），\u5728\u9ad8\u7070\u533a\u659c\u7387\u5c0f（\u4eae\u90e8\u53cd\u6b63\u770b\u5f97\u6e05，\u4e0d\u7528\u52a8）\u3002<\/p>\n

\u8fd9\u5c31\u662f\u66f2\u7ebf，\u4e5f\u5c31\u662f\u4eca\u665a\u7684\u4e3b\u89d2：<\/p>\n

\u4f3d\u9a6c\u6821\u6b63： g = 255 \u00b7 (f \/ 255)^\u03b3
\nLog \u53d8\u6362： g = c \u00b7 log10(1 + f)，\u5176\u4e2d c = 255 \/ log10(256) \u2248 96.34<\/p>\n

\n

\u4e8c\u3001\u6838\u5fc3\u539f\u7406：\u5e42\u51fd\u6570\u4e0e\u5bf9\u6570\u51fd\u6570\u7684\u5f62\u72b6\u9b54\u6cd5<\/h3>\n

2.1 \u4f3d\u9a6c\u6821\u6b63<\/h4>\n

\u628a\u50cf\u7d20\u503c\u5f52\u4e00\u5316\u5230 [0, 1]，\u505a\u5e42\u8fd0\u7b97，\u518d\u6620\u5c04\u56de [0, 255]：<\/p>\n

g = 255 \u00b7 (f \/ 255)^\u03b3<\/p>\n\n\u03b3 \u53d6\u503c\u66f2\u7ebf\u5f62\u72b6\u6548\u679c\u5178\u578b\u573a\u666f<\/tr>\n\n\n\n\n
\u03b3 < 1<\/td>\n\u4f4e\u7070\u533a\u659c\u7387 > 1，\u66f2\u7ebf\u4e0a\u51f8<\/td>\n\u63d0\u4eae\u6697\u90e8<\/td>\n\u6b20\u66dd\u4fee\u590d\u3001\u6697\u623f\u7167\u7247<\/td>\n<\/tr>\n
\u03b3 = 1<\/td>\n\u5bf9\u89d2\u76f4\u7ebf<\/td>\n\u6052\u7b49（\u4e0d\u53d8）<\/td>\n\u2014<\/td>\n<\/tr>\n
\u03b3 > 1<\/td>\n\u4f4e\u7070\u533a\u659c\u7387 < 1，\u66f2\u7ebf\u4e0b\u51f9<\/td>\n\u538b\u6697\u6697\u90e8\u3001\u4fdd\u62a4\u9ad8\u5149<\/td>\n\u9632\u6b62\u8fc7\u66dd\u3001HDR \u538b\u7f29<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4e24\u4e2a\u951a\u70b9\u662f\u7406\u89e3\u7684\u5173\u952e（\u5e42\u8fd0\u7b97\u7684\u6570\u5b66\u6027\u8d28）：
\n f = 0 \u2192 0^\u03b3 = 0 \u7eaf\u9ed1\u6c38\u8fdc\u4e0d\u53d8
\n f = 255 \u2192 1^\u03b3 = 1 \u7eaf\u767d\u6c38\u8fdc\u4e0d\u53d8
\n \u4e2d\u95f4\u5168\u90e8\u88ab\u66f2\u7ebf"\u5f2f"\u4e86：\u03b3 < 1 \u65f6\u4f4e\u7070\u533a\u88ab\u62c9\u4f38，\u03b3 > 1 \u65f6\u88ab\u538b\u7f29\u3002<\/p>\n

2.2 \u663e\u793a\u4f3d\u9a6c 2.2：\u4e3a\u4ec0\u4e48\u8fd9\u4e2a\u53c2\u6570\u65e0\u5904\u4e0d\u5728<\/h4>\n

\u5386\u53f2\u4e0e\u751f\u7406\u7684\u53cc\u91cd\u539f\u56e0：<\/p>\n

1. CRT \u663e\u793a\u5668\u7684\u7269\u7406\u54cd\u5e94\u8fd1\u4f3c\u5e42\u51fd\u6570 g \u221d f^2.2（\u7535\u538b \u2192 \u4eae\u5ea6\u7684\u975e\u7ebf\u6027）\u3002
\n2. \u4eba\u773c\u5bf9\u6697\u90e8\u66f4\u654f\u611f，sRGB \u6807\u51c6\u5e72\u8106"\u5c06\u9519\u5c31\u9519"：
\n \u56fe\u50cf\u5b58\u50a8\u65f6\u9884\u5148\u505a \u03b3 \u2248 1\/2.2 \u7684\u7f16\u7801，\u663e\u793a\u65f6\u786c\u4ef6\u81ea\u52a8\u505a \u03b3 = 2.2 \u89e3\u7801，
\n \u4e00\u6765\u4e00\u56de\u65e2\u8865\u507f\u4e86\u663e\u793a\u5668，\u53c8\u628a\u7f16\u7801\u7cbe\u5ea6\u96c6\u4e2d\u7ed9\u4e86\u4eba\u773c\u6700\u654f\u611f\u7684\u6697\u90e8\u3002<\/p>\n

\u6240\u4ee5\u4eca\u5929\u51e0\u4e4e\u6240\u6709\u56fe\u50cf\u90fd\u662f"\u4f3d\u9a6c\u538b\u7f29\u8fc7"\u7684\u3002\u76f8\u673a RAW \u76f4\u51fa\u504f\u6697\u3001\u6e38\u620f\u5f15\u64ce\u91cc\u51fa\u73b0 Gamma\/Linear \u8272\u5f69\u7a7a\u95f4\u5207\u6362\u3001\u663e\u793a\u5668\u6821\u8272\u8981\u8c03\u4f3d\u9a6c\u66f2\u7ebf\u2014\u2014\u5168\u662f\u540c\u4e00\u4e2a\u6570\u5b66\u3002<\/p>\n

2.3 Log \u53d8\u6362<\/h4>\n

\u5bf9\u6570\u51fd\u6570\u5929\u7136"\u538b\u5927\u4fdd\u5c0f"：<\/p>\n

g = c \u00b7 log10(1 + f)，c = 255 \/ log10(256) \u2248 96.34
\n （\u52a0 1 \u907f\u514d log10(0)；\u9664\u4ee5 log10(256) \u4fdd\u8bc1 f = 255 \u2192 g = 255）<\/p>\n\n\u53d8\u6362\u53c2\u6570\u6697\u90e8\u63d0\u5347\u4eae\u90e8\u5f71\u54cd\u53ef\u8c03\u6027<\/tr>\n\n\n\n
\u4f3d\u9a6c \u03b3<1<\/td>\n1 \u4e2a（\u03b3）<\/td>\n\u5f3a\u5ea6\u968f \u03b3 \u8fde\u7eed\u53ef\u8c03<\/td>\n\u540c\u6b65\u538b\u6697\u9ad8\u5149<\/td>\n\u5f3a<\/td>\n<\/tr>\n
Log<\/td>\n0 \u4e2a（\u5929\u751f\u56fa\u5b9a）<\/td>\n\u56fa\u5b9a\u5f3a\u63d0\u4eae<\/td>\n\u9ad8\u5149\u51e0\u4e4e\u4e0d\u52a8<\/td>\n\u5f31（\u53ef\u8c03 c）<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u76f4\u89c9\u5bf9\u6bd4：
\n Log \u53d8\u6362 \u2248 "\u4e00\u952e\u81ea\u52a8\u6321"\u2014\u2014\u65e0\u9700\u8c03\u53c2，\u6697\u90e8\u7acb\u523b\u53ef\u89c1，\u9002\u5408\u5feb\u901f\u9884\u89c8\u3001\u5085\u91cc\u53f6\u9891\u8c31\u663e\u793a；
\n \u4f3d\u9a6c\u6821\u6b63 \u2248 "\u624b\u52a8\u6321"\u2014\u2014\u03b3 \u7ed9\u4f60\u8fde\u7eed\u7684\u63a7\u5236\u8303\u56f4，\u9002\u5408\u7cbe\u7ec6\u4fee\u56fe\u4e0e\u81ea\u52a8\u5316 pipeline\u3002<\/p>\n

\"\u8bf7\u6dfb\u52a0\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u56fe 1：\u4e00\u5f20\u56fe\u770b\u61c2\u975e\u7ebf\u6027\u53d8\u6362\u3002\u5de6\u56fe\u4e3a\u6620\u5c04\u66f2\u7ebf\u65cf：\u03b3<1 \u7684\u66f2\u7ebf\u5728\u4f4e\u7070\u533a\u659c\u7387\u5927\u4e8e 1（\u6697\u90e8\u88ab\u62c9\u5f00），Log \u66f2\u7ebf\u6700\u9661；\u53f3\u56fe\u4e3a\u540c\u4e00\u6e10\u53d8\u6761\u7684\u5b9e\u9645\u6548\u679c，\u03b3=0.25 \u65f6\u6697\u7aef\u88ab\u663e\u8457\u62c9\u5bbd\u3002<\/p>\n

\n

\u4e09\u3001\u4ee3\u7801\u5b9e\u8df5<\/h3>\n

3.1 \u624b\u5199\u4f3d\u9a6c\u6821\u6b63（\u63a8\u8350\u7248\u672c）<\/h4>\n

import<\/span> numpy as<\/span> np<\/p>\n

def<\/span> gamma_correction<\/span>(<\/span>img,<\/span> gamma=<\/span>1.0<\/span>)<\/span>:<\/span>
\n """
\n \u4f3d\u9a6c\u6821\u6b63：g = 255 * (f\/255)^gamma
\n gamma < 1 \u63d0\u4eae\u6697\u90e8，gamma > 1 \u538b\u6697\u6697\u90e8\u3002
\n """<\/span>
\n norm =<\/span> img.<\/span>astype(<\/span>np.<\/span>float32)<\/span> \/<\/span> 255.0<\/span> # \u2460 \u5f52\u4e00\u5316\u5230 [0,1]<\/span>
\n out =<\/span> np.<\/span>power(<\/span>norm,<\/span> gamma)<\/span> *<\/span> 255.0<\/span> # \u2461 \u5e42\u8fd0\u7b97（float32 \u4e0d\u4f1a\u6ea2\u51fa）<\/span>
\n return<\/span> np.<\/span>clip(<\/span>np.<\/span>round<\/span>(<\/span>out)<\/span>,<\/span> 0<\/span>,<\/span> 255<\/span>)<\/span>.<\/span>astype(<\/span>np.<\/span>uint8)<\/span> # \u2462 \u843d\u56de uint8<\/span><\/p>\n

def<\/span> log_transform<\/span>(<\/span>img,<\/span> c=<\/span>None<\/span>)<\/span>:<\/span>
\n """Log \u53d8\u6362：g = c * log10(1 + f)"""<\/span>
\n if<\/span> c is<\/span> None<\/span>:<\/span>
\n c =<\/span> 255.0<\/span> \/<\/span> np.<\/span>log10(<\/span>1<\/span> +<\/span> 255<\/span>)<\/span> # \u2248 96.34，\u4fdd\u8bc1 f=255 \u2192 255<\/span>
\n out =<\/span> c *<\/span> np.<\/span>log10(<\/span>1.0<\/span> +<\/span> img.<\/span>astype(<\/span>np.<\/span>float32)<\/span>)<\/span>
\n return<\/span> np.<\/span>clip(<\/span>np.<\/span>round<\/span>(<\/span>out)<\/span>,<\/span> 0<\/span>,<\/span> 255<\/span>)<\/span>.<\/span>astype(<\/span>np.<\/span>uint8)<\/span><\/p>\n

\u5173\u952e\u7ec6\u8282：
\n 1. \u5f52\u4e00\u5316\u5230 [0,1] \u540e\u5e42\u8fd0\u7b97，\u7aef\u70b9 0^\u03b3=0\u30011^\u03b3=1 \u5929\u7136\u5728\u8303\u56f4\u5185，
\n \u6240\u4ee5\u4f3d\u9a6c\u6821\u6b63\u4e0d\u50cf\u7ebf\u6027\u53d8\u6362\u90a3\u6837\u6709"\u6ea2\u51fa"\u95ee\u9898\u2014\u2014\u4f46 round \u540e\u4ecd\u8981 clip \u515c\u5e95\u3002
\n 2. \u8d1f\u6570\u4e0d\u80fd\u505a\u6d6e\u70b9\u5e42（\u4f1a\u5f97\u5230 nan），\u8f93\u5165\u5fc5\u987b\u5148\u5f52\u4e00\u5316\u5230\u975e\u8d1f\u533a\u95f4\u3002
\n 3. dtype \u9677\u9631：uint8 \u76f4\u63a5\u53c2\u4e0e (f\/255)^\u03b3 \u4f1a\u5148\u6574\u6570\u9664\u6cd5\u5f52\u96f6\u2014\u2014
\n \u5fc5\u987b\u5148 astype(np.float32) \u518d\u9664\u3002<\/p>\n

3.2 LUT \u7248\u672c（\u975e\u7ebf\u6027\u70b9\u8fd0\u7b97\u7684\u5929\u7136\u642d\u6863）<\/h4>\n

\u4f3d\u9a6c\u662f"\u6bcf\u4e2a\u50cf\u7d20\u505a\u540c\u4e00\u4e2a\u6620\u5c04"，\u800c\u6620\u5c04\u53ea\u6709 256 \u79cd\u8f93\u5165\u2014\u2014\u5148\u7b97\u597d 256 \u4e2a\u8f93\u51fa，\u518d\u67e5\u8868：<\/p>\n

import<\/span> cv2
\nimport<\/span> numpy as<\/span> np<\/p>\n

def<\/span> gamma_by_lut<\/span>(<\/span>img,<\/span> gamma=<\/span>1.0<\/span>)<\/span>:<\/span>
\n lut =<\/span> np.<\/span>clip(<\/span>np.<\/span>round<\/span>(<\/span>
\n np.<\/span>power(<\/span>np.<\/span>arange(<\/span>256<\/span>,<\/span> dtype=<\/span>np.<\/span>float32)<\/span> \/<\/span> 255.0<\/span>,<\/span> gamma)<\/span> *<\/span> 255.0<\/span>
\n )<\/span>,<\/span> 0<\/span>,<\/span> 255<\/span>)<\/span>.<\/span>astype(<\/span>np.<\/span>uint8)<\/span>
\n return<\/span> cv2.<\/span>LUT(<\/span>img,<\/span> lut)<\/span><\/p>\n

\u8fd9\u7b14\u8d26\u5f88\u5212\u7b97：
\n NumPy \u7248：480\u00d7640 = 307,200 \u6b21\u6d6e\u70b9\u5e42\u8fd0\u7b97
\n LUT \u7248： 256 \u6b21\u5e42\u8fd0\u7b97（\u4e00\u6b21\u6027） + 307,200 \u6b21\u6574\u6570\u67e5\u8868
\n \u5e42\u8fd0\u7b97\u6bd4\u67e5\u8868\u8d35 1~2 \u4e2a\u6570\u91cf\u7ea7 \u2192 LUT \u5b9e\u6d4b\u5feb 10 \u500d\u4ee5\u4e0a（\u89c1\u7b2c\u4e94\u8282\u57fa\u51c6）\u3002<\/p>\n

3.3 \u4f3d\u9a6c\u7f51\u683c\u5b9e\u9a8c<\/h4>\n

for<\/span> g in<\/span> [<\/span>0.25<\/span>,<\/span> 0.45<\/span>,<\/span> 1.0<\/span>,<\/span> 2.0<\/span>,<\/span> 4.0<\/span>]<\/span>:<\/span>
\n out =<\/span> gamma_correction(<\/span>dark_img,<\/span> gamma=<\/span>g)<\/span><\/p>\n

\"\u8bf7\u6dfb\u52a0\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u56fe 2：\u540c\u4e00\u5f20\u6b20\u66dd\u6697\u56fe\u5728 \u03b3 = 0.25\/0.45\/1.0\/2.0\/4.0 \u4e0b\u7684\u8f93\u51fa\u4e0e\u76f4\u65b9\u56fe\u3002\u03b3<1 \u65f6\u76f4\u65b9\u56fe\u6574\u4f53\u53f3\u79fb\u4e14\u88ab"\u62c9\u5f00"，\u6697\u90e8\u7ec6\u8282\u8fdb\u5165\u53ef\u89c1\u533a\u95f4；\u03b3>1 \u65f6\u5206\u5e03\u5411\u6697\u7aef\u538b\u7f29\u5806\u79ef\u3002<\/p>\n

\n

\u56db\u3001\u4e09\u65b9\u6848\u5bf9\u6bd4：Log vs \u4f3d\u9a6c vs HE<\/h3>\n

\u6697\u56fe\u63d0\u4eae\u7684\u4e09\u4e2a\u5019\u9009，\u5404\u6709\u9c9c\u660e\u7684\u6027\u683c（HE \u4e3a 022 \u671f\u76f4\u65b9\u56fe\u5747\u8861\u5316）：<\/p>\n

\u5bf9\u540c\u4e00\u5f20\u6b20\u66dd\u56fe\u5b9e\u6d4b（mean \/ std）：
\n \u539f\u56fe： \u6697\u3001\u5bf9\u6bd4\u4f4e
\n Log \u53d8\u6362： \u6697\u90e8\u63d0\u4eae\u660e\u663e，\u76f4\u65b9\u56fe\u5e73\u6ed1\u53d8\u5f62\u3001\u5f62\u72b6\u4fdd\u7559
\n \u4f3d\u9a6c（\u81ea\u52a8）：\u63d0\u4eae\u5e45\u5ea6\u53ef\u63a7，std \u63d0\u5347\u6700\u81ea\u7136
\n HE： \u5bf9\u6bd4\u5ea6\u6700\u5f3a，\u4f46\u76f4\u65b9\u56fe\u88ab\u5f3a\u884c\u644a\u5e73\u2014\u2014\u566a\u58f0\u88ab\u4e00\u8d77\u653e\u5927（022 \u671f\u5df2\u9a8c\u8bc1\u7684\u526f\u4f5c\u7528）<\/p>\n

\u9009\u578b\u53e3\u8bc0：
\n \u00b7 \u5feb\u901f\u770b\u4e00\u773c\u6697\u90e8\u5728\u54ea\u513f \u2192 Log（\u514d\u8c03\u53c2）
\n \u00b7 \u4fee\u56fe\u8981\u7cbe\u7ec6\u63a7\u5236\u63d0\u4eae\u7a0b\u5ea6 \u2192 \u4f3d\u9a6c（\u03b3 \u8fde\u7eed\u53ef\u8c03）
\n \u00b7 \u8981\u7684\u662f"\u5bf9\u6bd4\u5ea6"\u800c\u4e0d\u662f"\u4eae\u5ea6" \u2192 HE \/ CLAHE（022\/023 \u671f）
\n \u00b7 \u566a\u58f0\u660e\u663e\u7684\u6697\u56fe \u2192 \u5148\u4e2d\u503c\/\u9ad8\u65af\u964d\u566a，\u518d\u4f3d\u9a6c\u63d0\u4eae<\/p>\n

\"\u8bf7\u6dfb\u52a0\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u56fe 3：Log\u3001\u4f3d\u9a6c（\u81ea\u52a8 \u03b3）\u3001HE \u4e09\u65b9\u6848\u5bf9\u6bd4\u3002\u4e0b\u6392\u76f4\u65b9\u56fe\u6e05\u6670\u53ef\u89c1：Log\/\u4f3d\u9a6c\u4fdd\u7559\u76f4\u65b9\u56fe\u5f62\u72b6\u505a\u5e73\u6ed1\u53d8\u5f62，HE \u5219\u5f3a\u884c\u644a\u5e73\u5bfc\u81f4\u68b3\u72b6\u5206\u5e03\u4e0e\u566a\u58f0\u653e\u5927\u3002<\/p>\n

\n

\u4e94\u3001\u81ea\u52a8\u4f3d\u9a6c：\u6309\u5747\u503c\u53cd\u89e3 \u03b3，\u514d\u8c03\u53c2<\/h3>\n

\u624b\u52a8\u8c03 \u03b3 \u9002\u5408\u4fee\u4e00\u5f20\u56fe；\u6279\u91cf\u5904\u7406 1000 \u5f20\u56fe\u65f6\u9700\u8981\u4e00\u4e2a"\u81ea\u52a8\u6321"\u3002\u65b9\u6cd5：\u5148\u5b9a\u76ee\u6807\u5747\u503c，\u53cd\u89e3 \u03b3\u3002<\/p>\n

def<\/span> auto_gamma<\/span>(<\/span>img,<\/span> target=<\/span>0.5<\/span>)<\/span>:<\/span>
\n """
\n \u76ee\u6807：\u8f93\u51fa\u5747\u503c = target（0.5 \u5373\u4e2d\u7070 128）
\n \u63a8\u5bfc：mean_in^gamma = target \u2192 gamma = log(target) \/ log(mean_in)
\n """<\/span>
\n mean_in =<\/span> img.<\/span>astype(<\/span>np.<\/span>float32)<\/span>.<\/span>mean(<\/span>)<\/span> \/<\/span> 255.0<\/span>
\n return<\/span> float<\/span>(<\/span>np.<\/span>log(<\/span>target)<\/span> \/<\/span> np.<\/span>log(<\/span>mean_in)<\/span>)<\/span><\/p>\n

\u5b9e\u6d4b（\u6df1\u5ea6\u6b20\u66dd\u56fe，mean \u2248 26\/255）：
\n target = 0.40 \u2192 \u81ea\u52a8 \u03b3 = 0.299 \u2192 \u5b9e\u9645\u8f93\u51fa mean = 91
\n target = 0.50 \u2192 \u81ea\u52a8 \u03b3 = 0.226 \u2192 \u5b9e\u9645\u8f93\u51fa mean = 114
\n target = 0.60 \u2192 \u81ea\u52a8 \u03b3 = 0.167 \u2192 \u5b9e\u9645\u8f93\u51fa mean = 137<\/p>\n

\u4e00\u4e2a\u5fc5\u987b\u77e5\u9053\u7684\u6570\u5b66\u771f\u76f8（\u672c\u7cfb\u5217\u5b9e\u6d4b\u9a8c\u8bc1）：
\n \u5b9e\u9645\u8f93\u51fa\u5747\u503c \u2260 \u7cbe\u786e\u7b49\u4e8e target\u3002
\n \u56e0\u4e3a auto_gamma \u7684\u63a8\u5bfc\u5047\u8bbe"\u5747\u503c\u7684\u4f3d\u9a6c = \u4f3d\u9a6c\u7684\u5747\u503c"，
\n \u800c\u5e42\u8fd0\u7b97\u662f\u975e\u7ebf\u6027\u51fd\u6570，\u5747\u503c\u8fd0\u7b97\u4e0d\u53ef\u4ea4\u6362（Jensen \u4e0d\u7b49\u5f0f：
\n \u5bf9\u51f9\u51fd\u6570 E[f(X)] \u2264 f(E[X])，\u03b3<1 \u65f6\u5b9e\u9645\u8f93\u51fa\u5747\u503c\u603b\u662f\u4f4e\u4e8e target）\u3002
\n \u5de5\u7a0b\u7ed3\u8bba：\u516c\u5f0f\u7ed9\u51fa\u7684 \u03b3 \u662f\u6781\u597d\u7684\u521d\u503c，\u7cbe\u786e\u5bf9\u9f50\u76ee\u6807\u5747\u503c
\n \u53ef\u518d\u7528 2~3 \u6b21\u4e8c\u5206\u8fed\u4ee3\u5fae\u8c03（\u6bcf\u8fed\u4ee3\u4e00\u6b21\u53ea\u82b1\u51e0\u6beb\u79d2）\u3002<\/p>\n

\"\u8bf7\u6dfb\u52a0\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u56fe 4：\u81ea\u52a8\u4f3d\u9a6c\u3002\u76ee\u6807\u5747\u503c\u8d8a\u9ad8 \u2192 \u81ea\u52a8 \u03b3 \u8d8a\u5c0f \u2192 \u6697\u90e8\u62c9\u5f97\u8d8a\u731b\u3002\u6ce8\u610f\u5b9e\u9645\u8f93\u51fa\u5747\u503c\u7565\u4f4e\u4e8e\u76ee\u6807\u503c（Jensen \u4e0d\u7b49\u5f0f\u7684\u6548\u679c），\u4f46\u4f5c\u4e3a\u514d\u8c03\u53c2\u521d\u503c\u5df2\u7ecf\u8db3\u591f\u597d\u3002<\/p>\n

\n

\u516d\u3001\u6027\u80fd\u57fa\u51c6：LUT \u662f\u975e\u7ebf\u6027\u53d8\u6362\u7684\u6700\u4f73\u62cd\u6863<\/h3>\n

480\u00d7640 \u7070\u5ea6\u56fe，\u03b3 = 0.5，\u4e09\u79cd\u5b9e\u73b0\u5b9e\u6d4b：<\/p>\n

Python \u53cc\u5c42\u5faa\u73af（\u9010\u50cf\u7d20\u67e5\u8868）： 114.6 ms
\nNumPy \u5411\u91cf\u5316（np.power）： 3.69 ms
\nLUT \u67e5\u8868（cv2.LUT）： 0.27 ms<\/p>\n

\u4e00\u81f4\u6027\u68c0\u67e5：
\n LUT vs NumPy：max|\u0394| = 0，\u5b8c\u5168\u4e00\u81f4
\n loop vs NumPy：max|\u0394| \u2264 1（\u56db\u820d\u4e94\u5165\u8def\u5f84\u5dee\u5f02）<\/p>\n\n\u5b9e\u73b0\u65b9\u5f0f\u8017\u65f6\u8bf4\u660e<\/tr>\n\n\n\n\n
Python \u5faa\u73af<\/td>\n~115 ms<\/td>\n\u4ec5\u6559\u5b66\u6f14\u793a<\/td>\n<\/tr>\n
NumPy \u5411\u91cf\u5316<\/td>\n~3.7 ms<\/td>\n30 \u4e07\u6b21\u6d6e\u70b9\u5e42，\u901a\u7528\u4f46\u6d6a\u8d39<\/td>\n<\/tr>\n
LUT \u67e5\u8868<\/td>\n~0.27 ms<\/td>\n256 \u6b21\u5e42 + \u6574\u6570\u67e5\u8868，\u5feb 13 \u500d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u7ed3\u8bba：\u4efb\u4f55"\u8f93\u5165\u8f93\u51fa\u90fd\u53ea\u6709 256 \u79cd\u53d6\u503c"\u7684\u70b9\u8fd0\u7b97（\u4f3d\u9a6c\/Log\/\u7070\u5ea6\u7a97\/LUT \u6ee4\u955c），
\n \u90fd\u5e94\u8be5\u7528\u67e5\u8868\u5b9e\u73b0\u3002\u8fd9\u4e5f\u662f 027 \u671f\u300a\u67e5\u627e\u8868（LUT）\u52a0\u901f\u300b\u7684\u9884\u544a\u4e3b\u7ebf\u3002<\/p>\n

\"\u8bf7\u6dfb\u52a0\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u56fe 5：\u4e09\u65b9\u6848\u6027\u80fd\u5bf9\u6bd4（\u5bf9\u6570\u5750\u6807）\u3002LUT \u628a"\u9010\u50cf\u7d20\u5e42\u8fd0\u7b97"\u964d\u4e3a"\u9010\u50cf\u7d20\u67e5\u8868"，\u5b9e\u6d4b\u6bd4 NumPy \u5411\u91cf\u5316\u5feb\u7ea6 13 \u500d，\u4e14\u8f93\u51fa\u9010\u50cf\u7d20\u4e00\u81f4\u3002<\/p>\n

\n

\u4e03\u3001\u5f69\u8272\u56fe\u4e09\u65b9\u6848：BGR \u9010\u901a\u9053 \/ HSV \u53ea\u52a8 V \/ Lab \u53ea\u52a8 L<\/h3>\n

\u4e0e 024 \u671f\u540c\u6837\u7684\u8001\u95ee\u9898：\u5f69\u8272\u56fe\u4e0d\u80fd\u65e0\u8111\u5957\u516c\u5f0f\u3002\u8fd9\u6b21\u505a \u03b3 = 1.6 \u7684\u538b\u6697\u5b9e\u9a8c（\u4e0e 024 \u671f\u7684\u589e\u4eae\u5f62\u6210\u4e92\u8865\u89c6\u89d2）：<\/p>\n

\u65b9\u6848 A：BGR \u4e09\u901a\u9053\u9010\u901a\u9053\u4f3d\u9a6c
\n \u0394H = 1.93\u00b0 \u0394S = +36.2 \u0394V = \u221222.7
\n \u4eae\u5ea6\u538b\u6697\u540e\u6697\u901a\u9053\u5360\u6bd4\u76f8\u5bf9\u4e0a\u5347，\u9971\u548c\u5ea6\u53cd\u800c\u589e\u52a0，\u8272\u76f8\u8f7b\u5fae\u6f02\u79fb\u3002<\/p>\n

\u65b9\u6848 B：HSV \u53ea\u52a8 V \u901a\u9053
\n \u0394H = 0.00\u00b0 \u0394S = +0.7 \u0394V = \u221222.7
\n \u8272\u76f8\u5b8c\u5168\u4e0d\u52a8，\u9971\u548c\u5ea6\u51e0\u4e4e\u4e0d\u53d8\u2014\u2014\u6700\u7a33\u3002<\/p>\n

\u65b9\u6848 C：Lab \u53ea\u52a8 L \u901a\u9053
\n \u0394H = 1.36\u00b0 \u0394S = +58.0 \u0394V = \u221239.6
\n \u611f\u77e5\u4eae\u5ea6\u538b\u5f97\u6700\u6df1，\u9971\u548c\u5ea6\u5927\u5e45\u4e0a\u5347（L \u88ab\u538b\u800c\u8272\u5ea6\u5206\u91cf\u4e0d\u53d8），
\n \u89c6\u89c9\u4e0a"\u53d8\u6d53\u53d8\u6697"，\u6709\u65f6\u53cd\u800c\u662f\u60f3\u8981\u7684\u7535\u5f71\u611f\u3002<\/p>\n

# \u65b9\u6848 B：HSV \u53ea\u52a8 V（\u63a8\u8350）<\/span>
\nhsv =<\/span> cv2.<\/span>cvtColor(<\/span>img,<\/span> cv2.<\/span>COLOR_BGR2HSV)<\/span>
\nhsv[<\/span>:<\/span>,<\/span> :<\/span>,<\/span> 2<\/span>]<\/span> =<\/span> gamma_by_lut(<\/span>hsv[<\/span>:<\/span>,<\/span> :<\/span>,<\/span> 2<\/span>]<\/span>,<\/span> gamma=<\/span>1.6<\/span>)<\/span>
\nout =<\/span> cv2.<\/span>cvtColor(<\/span>hsv,<\/span> cv2.<\/span>COLOR_HSV2BGR)<\/span><\/p>\n

# \u65b9\u6848 C：Lab \u53ea\u52a8 L（\u611f\u77e5\u81ea\u7136，\u8272\u5f69\u53d8\u6d53）<\/span>
\nlab =<\/span> cv2.<\/span>cvtColor(<\/span>img,<\/span> cv2.<\/span>COLOR_BGR2LAB)<\/span>
\nlab[<\/span>:<\/span>,<\/span> :<\/span>,<\/span> 0<\/span>]<\/span> =<\/span> gamma_by_lut(<\/span>lab[<\/span>:<\/span>,<\/span> :<\/span>,<\/span> 0<\/span>]<\/span>,<\/span> gamma=<\/span>1.6<\/span>)<\/span>
\nout =<\/span> cv2.<\/span>cvtColor(<\/span>lab,<\/span> cv2.<\/span>COLOR_LAB2BGR)<\/span><\/p>\n

\u9009\u578b\u5efa\u8bae（\u4e0e 024 \u671f\u7ed3\u8bba\u4e00\u8109\u76f8\u627f）：
\n \u00b7 \u4e0d\u60f3\u504f\u8272\u3001\u53ea\u60f3\u52a8\u4eae\u5ea6 \u2192 HSV \u53ea\u52a8 V
\n \u00b7 \u60f3\u8981"\u538b\u6697\u540e\u989c\u8272\u66f4\u6d53"\u7684\u8d28\u611f \u2192 Lab \u53ea\u52a8 L（\u7535\u5f71\u8c03\u8272\u5e38\u7528）
\n \u00b7 \u9010\u901a\u9053 BGR \u4f3d\u9a6c \u2192 \u4ec5\u5728\u660e\u786e\u77e5\u9053\u8981\u4ec0\u4e48\u65f6\u4f7f\u7528<\/p>\n

\"\u8bf7\u6dfb\u52a0\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u56fe 6：\u5f69\u8272\u56fe\u4f3d\u9a6c\u4e09\u65b9\u6848\u5bf9\u6bd4（\u03b3=1.6 \u538b\u6697）\u3002B \u65b9\u6848\u8272\u76f8\u96f6\u6f02\u79fb；C \u65b9\u6848\u9971\u548c\u5ea6\u5927\u5e45\u4e0a\u5347\u5e26\u6765"\u53d8\u6d53"\u8d28\u611f；A \u65b9\u6848\u4ecb\u4e8e\u4e24\u8005\u4e4b\u95f4\u4f46\u8272\u76f8\u6709\u8f7b\u5fae\u6f02\u79fb\u3002<\/p>\n

\n

\u516b\u3001\u4ea4\u4e92\u5f0f\u4f3d\u9a6c\u8c03\u8282\u5668（\u672c\u5730 Trackbar）<\/h3>\n

import<\/span> cv2
\nimport<\/span> numpy as<\/span> np<\/p>\n

def<\/span> gamma_by_lut<\/span>(<\/span>img,<\/span> gamma=<\/span>1.0<\/span>)<\/span>:<\/span>
\n lut =<\/span> np.<\/span>clip(<\/span>np.<\/span>round<\/span>(<\/span>
\n np.<\/span>power(<\/span>np.<\/span>arange(<\/span>256<\/span>,<\/span> dtype=<\/span>np.<\/span>float32)<\/span> \/<\/span> 255.0<\/span>,<\/span> gamma)<\/span> *<\/span> 255.0<\/span>
\n )<\/span>,<\/span> 0<\/span>,<\/span> 255<\/span>)<\/span>.<\/span>astype(<\/span>np.<\/span>uint8)<\/span>
\n return<\/span> cv2.<\/span>LUT(<\/span>img,<\/span> lut)<\/span><\/p>\n

img =<\/span> cv2.<\/span>imread(<\/span>'your_photo.jpg'<\/span>)<\/span>
\nwindow =<\/span> 'Gamma Correction (q to quit)'<\/span>
\ncv2.<\/span>namedWindow(<\/span>window,<\/span> cv2.<\/span>WINDOW_NORMAL)<\/span>
\ncv2.<\/span>createTrackbar(<\/span>'gamma x100'<\/span>,<\/span> window,<\/span> 100<\/span>,<\/span> 400<\/span>,<\/span> lambda<\/span> x:<\/span> None<\/span>)<\/span> # 0.1 ~ 4.0<\/span><\/p>\n

while<\/span> True<\/span>:<\/span>
\n g =<\/span> cv2.<\/span>getTrackbarPos(<\/span>'gamma x100'<\/span>,<\/span> window)<\/span> \/<\/span> 100.0<\/span>
\n if<\/span> g <<\/span> 0.1<\/span>:<\/span>
\n g =<\/span> 0.1<\/span>
\n cv2.<\/span>imshow(<\/span>window,<\/span> gamma_by_lut(<\/span>img,<\/span> g)<\/span>)<\/span>
\n if<\/span> cv2.<\/span>waitKey(<\/span>30<\/span>)<\/span> &<\/span> 0xFF<\/span> ==<\/span> ord<\/span>(<\/span>'q'<\/span>)<\/span>:<\/span>
\n break<\/span><\/p>\n

cv2.<\/span>destroyAllWindows(<\/span>)<\/span><\/p>\n

\u26a0\ufe0f \u8fd9\u6bb5\u4ee3\u7801\u9700\u8981\u672c\u5730\u6709\u56fe\u5f62\u754c\u9762；\u7531\u4e8e LUT \u9884\u8ba1\u7b97\u53ea\u9700 256 \u6b21\u5e42\u8fd0\u7b97，\u62d6\u52a8\u6ed1\u6746\u4e5f\u662f\u5b9e\u65f6\u5e27\u7387\u3002<\/p>\n

\"\u8bf7\u6dfb\u52a0\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u56fe 7：\u53c2\u6570\u7f51\u683c\u9884\u89c8\u3002\u4e09\u884c\u5206\u522b\u662f：\u7eaf\u4f3d\u9a6c\u3001"\u4f3d\u9a6c+\u7ebf\u6027\u538b\u5bf9\u6bd4"\u7ec4\u5408\u3001Log\u2192\u4f3d\u9a6c\u7ea7\u8054\u3002\u7ea7\u8054\u4f7f\u7528\u662f\u4fee\u56fe\u8f6f\u4ef6"\u66f2\u7ebf\u5de5\u5177"\u7684\u96cf\u5f62\u3002<\/p>\n

\n

\u4e5d\u3001Pipeline \u5b9a\u4f4d：\u7ebf\u6027 vs \u4f3d\u9a6c，\u6697\u56fe\u7b2c\u4e00\u6b65\u9009\u8c01<\/h3>\n

\u628a\u4e24\u6761\u8def\u7ebf\u63a5\u5230\u540c\u4e00\u6761\u589e\u5f3a\u94fe\u4e0a\u6b63\u9762\u4ea4\u950b（\u540e\u7aef\u90fd\u662f 023 \u671f\u7684 CLAHE）：<\/p>\n

\u8def\u7ebf\u2460 \u7ebf\u6027（024 \u671f\u6ed1\u6746\u7ec4\u5408）：g = 1.4\u00b7f + 55
\n \u2192 \u63d0\u4eae\u540e mean = 78，std = 16 \u2014\u2014 \u4eae\u5ea6\u4e0a\u6765\u4e86，\u4f46\u5bf9\u6bd4\u5ea6\u5e73\u5eb8<\/p>\n

\u8def\u7ebf\u2461 \u4f3d\u9a6c（\u672c\u671f\u81ea\u52a8 \u03b3 \u2248 0.3）：g = 255\u00b7(f\/255)^\u03b3
\n \u2192 \u63d0\u4eae\u540e mean = 115，std = 40 \u2014\u2014 \u4eae\u5ea6\u5bf9\u6bd4\u5ea6\u53cc\u4f18<\/p>\n

\u4e24\u6761\u8def\u7ebf\u518d\u63a5 CLAHE：
\n \u7ebf\u6027 + CLAHE：mean = 85，std = 22
\n \u4f3d\u9a6c + CLAHE：mean = 116，std = 41<\/p>\n

\u7ed3\u8bba（028 \u671f\u81ea\u52a8\u7167\u7247\u589e\u5f3a\u5668\u7684\u9009\u578b\u4f9d\u636e）：
\n \u6697\u56fe\u7684"\u7b2c\u4e00\u6b65\u7c97\u8c03"\u5e94\u8be5\u4f18\u5148\u8003\u8651\u4f3d\u9a6c\u6821\u6b63：
\n \u7ebf\u6027\u53d8\u6362\u63d0\u4eae = \u6574\u4f53\u5e73\u79fb，\u6697\u90e8\u7ec6\u8282\u7684\u76f8\u5bf9\u5c42\u6b21\u4e0d\u53d8；
\n \u4f3d\u9a6c\u63d0\u4eae = \u975e\u7ebf\u6027\u62c9\u4f38，\u6697\u90e8\u5c42\u6b21\u88ab\u771f\u6b63"\u5c55\u5f00"，
\n \u540e\u7eed CLAHE \u7684\u5c40\u90e8\u589e\u5f3a\u624d\u6709\u6599\u53ef\u6316\u3002
\n \u7ebf\u6027\u53d8\u6362\u7684\u7528\u6b66\u4e4b\u5730\u662f"\u4eae\u5ea6\u5df2\u7ecf\u6b63\u5e38\u3001\u53ea\u5dee\u4e00\u70b9\u5bf9\u6bd4\u5ea6"\u7684\u56fe\u3002<\/p>\n

\"\u8bf7\u6dfb\u52a0\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u56fe 8：\u6697\u56fe\u4fee\u590d\u4e24\u6761\u8def\u7ebf\u3002\u4e0a\u7ebf\u6027\u8def\u7ebf\u63d0\u4eae\u540e\u6574\u4f53\u7070\u8499（std=16），\u4e0b\u4f3d\u9a6c\u8def\u7ebf\u6697\u90e8\u5c42\u6b21\u88ab\u771f\u6b63\u5c55\u5f00（std=40），\u63a5 CLAHE \u540e\u5dee\u8ddd\u8fdb\u4e00\u6b65\u62c9\u5927\u3002<\/p>\n

\n

\u5341\u3001\u53ef\u89c6\u5316\u5b9e\u9a8c\u603b\u89c8<\/h3>\n

\u672c\u6b21\u5171 8 \u4e2a\u5b9e\u9a8c，\u5bf9\u5e94 8 \u5f20\u53ef\u89c6\u5316\u56fe：<\/p>\n\n#\u5b9e\u9a8c\u56fe\u6587\u4ef6\u9a8c\u8bc1\u76ee\u6807<\/tr>\n\n\n\n\n\n\n\n\n\n
1<\/td>\n\u03b3 \u66f2\u7ebf\u65cf\u4e0e\u6e10\u53d8\u6761<\/td>\n025_curves_principle.png<\/td>\n\u4e00\u5f20\u56fe\u770b\u61c2\u66f2\u7ebf\u5f62\u72b6<\/td>\n<\/tr>\n
2<\/td>\n\u4f3d\u9a6c\u7f51\u683c<\/td>\n025_gamma_grid.png<\/td>\n\u03b3<1 \u63d0\u4eae \/ \u03b3>1 \u538b\u6697<\/td>\n<\/tr>\n
3<\/td>\nLog vs \u4f3d\u9a6c vs HE<\/td>\n025_log_vs_gamma.png<\/td>\n\u4e09\u65b9\u6848\u6027\u683c\u5bf9\u6bd4<\/td>\n<\/tr>\n
4<\/td>\n\u81ea\u52a8\u4f3d\u9a6c<\/td>\n025_auto_gamma.png<\/td>\n\u514d\u8c03\u53c2 + Jensen \u504f\u5dee<\/td>\n<\/tr>\n
5<\/td>\n\u6027\u80fd\u4e0e\u4e00\u81f4\u6027<\/td>\n025_lut_benchmark.png<\/td>\nLUT \u5feb 13 \u500d<\/td>\n<\/tr>\n
6<\/td>\n\u5f69\u8272\u56fe\u4e09\u65b9\u6848<\/td>\n025_color_gamma.png<\/td>\nBGR\/HSV\/Lab \u8c01\u6700\u7a33<\/td>\n<\/tr>\n
7<\/td>\n\u53c2\u6570\u7f51\u683c<\/td>\n025_slider_grid.png<\/td>\n\u7b49\u6548\u6ed1\u6746 + \u7ea7\u8054\u7ec4\u5408<\/td>\n<\/tr>\n
8<\/td>\nPipeline \u5b9a\u4f4d<\/td>\n025_pipeline_position.png<\/td>\n\u7ebf\u6027 vs \u4f3d\u9a6c\u9009\u578b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4ee3\u7801\u8fd0\u884c\u540e，\u4e0a\u8ff0 PNG \u5168\u90e8\u81ea\u52a8\u751f\u6210\u5230 code\/output\/ \u76ee\u5f55\u3002<\/p>\n

\n

\u5341\u4e00\u3001\u4ea7\u54c1\u63a8\u8350<\/h3>\n\n\u5c42\u7ea7\u79fb\u52a8\u7aef App\u4e13\u4e1a\u7ea7\u5de5\u5177\u5f00\u6e90\/\u514d\u8d39\u9002\u7528\u573a\u666f<\/tr>\n\n\n\n\n\n
\u4f3d\u9a6c\/\u66f2\u7ebf\u8c03\u6574<\/td>\nSnapseed（\u66f2\u7ebf）\u3001Lightroom Mobile（\u8272\u8c03\u66f2\u7ebf）\u3001VSCO<\/td>\nAdobe Photoshop（\u66dd\u5149\/\u4f3d\u9a6c\/\u66f2\u7ebf）\u3001Capture One\u3001DaVinci Resolve（Gamma \u8272\u5f69\u7a7a\u95f4）<\/td>\nGIMP（\u66f2\u7ebf）\u3001Darktable\u3001RawTherapee<\/td>\n\u4fee\u56fe\u8c03\u8272<\/td>\n<\/tr>\n
\u663e\u793a\u6821\u8272<\/td>\n\u624b\u673a\u7cfb\u7edf\u663e\u793a\u8272\u5f69\u6a21\u5f0f（P3\/sRGB \u5207\u6362）<\/td>\nDisplayCAL + \u6821\u8272\u4eea\u3001X-Rite i1Display\u3001Calibrite<\/td>\nDisplayCAL（\u5f00\u6e90）<\/td>\n\u663e\u793a\u5668\u4f3d\u9a6c\u6821\u51c6<\/td>\n<\/tr>\n
\u79d1\u5b66\u53ef\u89c6\u5316<\/td>\n\u2014<\/td>\nImageJ\/Fiji\u3001MATLAB（imadjust）<\/td>\nImageJ（\u514d\u8d39）<\/td>\n\u9891\u8c31\/\u533b\u5b66\u56fe\u663e\u793a<\/td>\n<\/tr>\n
\u5f71\u89c6\u8c03\u8272<\/td>\n\u2014<\/td>\nDaVinci Resolve（Gamma \u6807\u7b7e\u9875）\u3001Baselight<\/td>\n\u2014<\/td>\nLog \u7d20\u6750\u8fd8\u539f<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u9009\u62e9\u5efa\u8bae：
\n \u00b7 \u624b\u673a\u5feb\u901f\u63d0\u4eae\u6697\u56fe：Snapseed "\u66f2\u7ebf"\u5de5\u5177\u62c9\u4e00\u6839 \u03b3<1 \u7684\u4e0a\u51f8\u66f2\u7ebf，\u5c31\u662f\u4eca\u665a\u7684\u6570\u5b66\u3002
\n \u00b7 \u663e\u793a\u5668\u6821\u8272：DisplayCAL \u514d\u8d39\u4e13\u4e1a，\u628a\u663e\u793a\u5668\u4f3d\u9a6c\u6821\u5230 sRGB（\u22482.2）\u6807\u51c6\u3002
\n \u00b7 \u5f71\u89c6\u884c\u4e1a：DaVinci Resolve \u7684 Gamma \u8272\u5f69\u7a7a\u95f4\u5c31\u662f\u5b57\u9762\u610f\u4e49\u7684\u672c\u8bfe\u4e3b\u89d2\u3002<\/p>\n

\n

\u5341\u4e8c\u3001\u5e38\u89c1\u95ee\u9898 FAQ<\/h3>\n

Q1：\u03b3 = 2.2 \u5230\u5e95\u662f\u63d0\u4eae\u8fd8\u662f\u538b\u6697？
\nA：\u770b\u65b9\u5411\u3002\u5bf9\u56fe\u50cf\u505a g = (f)^2.2 \u662f\u538b\u6697（\u7f16\u7801\u65b9\u5411）；\u663e\u793a\u5668\u786c\u4ef6\u505a \u03b3=2.2 \u89e3\u7801\u662f\u8fd8\u539f\u3002\u65e5\u5e38\u8bf4"\u663e\u793a\u5668\u4f3d\u9a6c 2.2"\u6307\u7684\u662f\u89e3\u7801\u7279\u6027，\u800c\u4fee\u56fe\u8f6f\u4ef6\u91cc\u7684"\u4f3d\u9a6c\u6ed1\u6746 < 1"\u662f\u63d0\u4eae\u7f16\u7801\u3002\u6293\u4f4f"\u03b3 \u4f5c\u7528\u4e8e\u8c01"\u5c31\u4e0d\u4f1a\u6df7\u3002<\/p>\n

Q2：\u4f3d\u9a6c\u6821\u6b63\u4f1a\u6709 uint8 \u6ea2\u51fa\u95ee\u9898\u5417？
\nA：\u5f52\u4e00\u5316\u540e\u5e42\u8fd0\u7b97\u7aef\u70b9\u5929\u7136\u5728 [0,1] \u5185，\u7406\u8bba\u4e0d\u6ea2\u51fa\u3002\u4f46\u6d6e\u70b9\u7cbe\u5ea6\u548c round \u4ecd\u5efa\u8bae\u4fdd\u7559 clip \u515c\u5e95\u3002\u771f\u6b63\u7684\u9677\u9631\u662f dtype：uint8 \u5148\u9664 255 \u4f1a\u6574\u6570\u5f52\u96f6，\u5fc5\u987b\u5148\u8f6c float32\u3002<\/p>\n

Q3：\u81ea\u52a8\u4f3d\u9a6c\u7684\u8f93\u51fa\u5747\u503c\u4e3a\u4ec0\u4e48\u8fbe\u4e0d\u5230 target？
\nA：\u63a8\u5bfc\u5047\u8bbe"\u5747\u503c\u5148\u53d6\u518d\u5e42"\u7b49\u4e8e"\u5148\u5e42\u518d\u53d6\u5747\u503c"，\u4f46\u5e42\u51fd\u6570\u975e\u7ebf\u6027，\u4e24\u8005\u4e0d\u7b49（Jensen \u4e0d\u7b49\u5f0f：\u03b3<1 \u65f6\u5b9e\u9645\u5747\u503c\u504f\u4f4e）\u3002\u5de5\u7a0b\u4e0a\u628a\u516c\u5f0f\u7ed3\u679c\u5f53\u521d\u503c，\u4e8c\u5206\u8fed\u4ee3 2~3 \u6b21\u5373\u53ef\u7cbe\u786e\u5bf9\u9f50\u3002<\/p>\n

Q4：Log \u53d8\u6362\u548c\u4f3d\u9a6c < 1 \u6548\u679c\u597d\u50cf，\u600e\u4e48\u9009？
\nA：Log \u514d\u8c03\u53c2\u3001\u9ad8\u5149\u51e0\u4e4e\u4e0d\u52a8，\u9002\u5408"\u770b\u4e00\u773c"\u548c\u5085\u91cc\u53f6\u9891\u8c31\u663e\u793a；\u4f3d\u9a6c\u53ef\u63a7\u6027\u5f3a，\u9002\u5408\u6279\u91cf pipeline \u548c\u7cbe\u7ec6\u4fee\u56fe\u3002\u9700\u8981\u4e25\u683c\u4fdd\u6301\u7aef\u70b9\u65f6\u7528\u4f3d\u9a6c（0\u21920\u3001255\u2192255），Log \u4e5f\u6ee1\u8db3（log10(1)=0），\u4f46\u4e2d\u95f4\u884c\u4e3a\u4e0d\u540c\u3002<\/p>\n

Q5：\u4e3a\u4ec0\u4e48\u5f69\u8272\u56fe\u505a Lab \u4f3d\u9a6c\u540e\u989c\u8272"\u53d8\u6d53"\u4e86？
\nA：\u4f3d\u9a6c\u53ea\u538b L（\u660e\u5ea6）\u901a\u9053，a\/b（\u8272\u5ea6）\u5206\u91cf\u6570\u503c\u4e0d\u53d8\u3002\u660e\u5ea6\u964d\u4e86\u3001\u8272\u5ea6\u5360\u6bd4\u76f8\u5bf9\u5347\u9ad8，\u89c6\u89c9\u9971\u548c\u5ea6\u5c31\u4e0a\u5347（\u5b9e\u6d4b \u0394S=+58）\u3002\u8fd9\u65e2\u662f\u9677\u9631\u4e5f\u662f\u7279\u6027\u2014\u2014\u7535\u5f71\u8c03\u8272\u91cc\u7684"\u538b\u6697\u63d0\u9971\u548c"\u5c31\u662f\u8fd9\u4e2a\u539f\u7406\u3002<\/p>\n

Q6：\u4f3d\u9a6c\u548c 022\/023 \u671f\u7684 HE\/CLAHE \u6709\u4ec0\u4e48\u672c\u8d28\u533a\u522b？
\nA：\u4f3d\u9a6c\/Log \u662f\u56fa\u5b9a\u6570\u5b66\u5f62\u72b6\u7684\u70b9\u8fd0\u7b97（\u4e0e\u56fe\u50cf\u5185\u5bb9\u65e0\u5173）；HE\/CLAHE \u662f\u6570\u636e\u9a71\u52a8\u7684（\u53d8\u6362\u66f2\u7ebf\u7531\u76f4\u65b9\u56fe\u7b97\u51fa）\u3002\u524d\u8005\u53ef\u63a7\u53ef\u590d\u73b0，\u540e\u8005\u81ea\u9002\u5e94\u4f46\u53ef\u80fd\u653e\u5927\u566a\u58f0\u3002\u589e\u5f3a\u94fe\u91cc\u5e38\u5e38\u662f\u4f3d\u9a6c\u5148\u7c97\u8c03\u3001CLAHE \u518d\u7cbe\u4fee\u3002<\/p>\n

\n

\u5341\u4e09\u3001\u8bfe\u540e\u7ec3\u4e60<\/h3>\n
  • \u57fa\u7840\u7ec3\u4e60：\u62cd\u4e00\u5f20\u5ba4\u5185\u6b20\u66dd\u7167\u7247，\u5206\u522b\u7528 \u03b3=0.5\u3001Log\u3001HE \u4e09\u79cd\u65b9\u6cd5\u63d0\u4eae，\u653e\u5927\u6697\u90e8\u533a\u57df\u5bf9\u6bd4\u566a\u58f0\u53ef\u89c1\u5ea6，\u9a8c\u8bc1"\u4f3d\u9a6c\u4fdd\u5f62\u3001HE \u653e\u566a"\u7684\u7ed3\u8bba\u3002<\/li>\n
  • \u8fdb\u9636\u7ec3\u4e60：\u7ed9 auto_gamma \u52a0\u4e0a\u4e8c\u5206\u8fed\u4ee3，\u8ba9\u5b9e\u9645\u8f93\u51fa\u5747\u503c\u4e0e target \u7684\u8bef\u5dee < 1 \u4e2a\u7070\u5ea6\u7ea7，\u6d4b\u8bd5 10 \u5f20\u4e0d\u540c\u4eae\u5ea6\u7684\u56fe\u5404\u9700\u51e0\u6b21\u8fed\u4ee3\u3002<\/li>\n
  • \u6311\u6218\u9898：\u5b9e\u73b0"\u81ea\u52a8 Log \u53d8\u6362"：\u6839\u636e\u56fe\u50cf\u5747\u503c\u81ea\u9002\u5e94\u8ba1\u7b97\u5e38\u6570 c（\u63d0\u793a：\u8ba9\u8f93\u51fa\u5747\u503c\u843d\u5728 100~150 \u533a\u95f4），\u5e76\u4e0e\u81ea\u52a8\u4f3d\u9a6c\u5728 5 \u7c7b\u56fe\u50cf\u4e0a\u5bf9\u6bd4\u9c81\u68d2\u6027\u3002<\/li>\n
    \n

    \u5341\u56db\u3001\u5b8c\u6574\u53ef\u8fd0\u884c\u811a\u672c<\/h3>\n

    # \u73af\u5883\u8981\u6c42<\/span>
    \nPython 3.8<\/span>+
    \nOpenCV 4.5<\/span>+ \/ 5<\/span>.x
    \nNumPy 1.20<\/span>+
    \nMatplotlib 3.4<\/span>+<\/p>\n

    # \u8fd0\u884c<\/span>
    \npython code\/025_gamma_log_transform.py<\/p>\n

    \u8fd0\u884c\u540e code\/output\/ \u4e0b\u4f1a\u751f\u6210 8 \u5f20 PNG，\u5bf9\u5e94\u672c\u8282\u5b9e\u9a8c\u3002<\/p>\n

    \n

    \u4e0b\u671f\u9884\u544a（\u7b2c 026 \u671f）：\u5206\u6bb5\u7ebf\u6027\u53d8\u6362\u2014\u2014\u7070\u5ea6\u7a97\u4e0e\u9636\u68af\u53d8\u6362\u3002\u628a\u76f4\u7ebf\u6298\u6210\u51e0\u6bb5，\u5b9e\u73b0"\u53ea\u62c9\u4f38\u4e2d\u95f4\u8c03\u3001\u51bb\u7ed3\u4e24\u7aef"\u7684\u7cbe\u7ec6\u63a7\u5236，\u533b\u5b66\u5f71\u50cf CT \u7684\u7a97\u5bbd\u7a97\u4f4d\u6b63\u662f\u5b83\u7684\u5dc5\u5cf0\u5e94\u7528\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

    \u7b2c 025 \u671f | \u56fe\u50cf\u5904\u7406\u7b97\u6cd5\u5165\u95e8\u7cfb\u5217 | 2026-09-03
    \n\u5173\u952e\u8bcd：\u4f3d\u9a6c\u6821\u6b63\u3001Log \u53d8\u6362\u3001\u975e\u7ebf\u6027\u70b9\u8fd0\u7b97\u3001\u81ea\u52a8\u4f3d\u9a6c\u3001LUT \u52a0\u901f\u3001sRGB \u663e\u793a\u4f3d\u9a6c\u3001\u6697\u56fe\u589e\u5f3a
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