{"id":59430,"date":"2026-01-13T21:09:26","date_gmt":"2026-01-13T13:09:26","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/59430.html"},"modified":"2026-01-13T21:09:26","modified_gmt":"2026-01-13T13:09:26","slug":"%e5%9f%ba%e4%ba%8e%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e7%9a%84ai%e5%bc%b1%e5%85%89%e5%9b%be%e5%83%8f%e5%a2%9e%e5%bc%bahep%e7%b3%bb%e7%bb%9f%e6%ba%90%e7%a0%81","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/59430.html","title":{"rendered":"\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u795e\u7ecf\u7f51\u7edc\u7684AI\u5f31\u5149\u56fe\u50cf\u589e\u5f3aHEP\u7cfb\u7edf\u6e90\u7801"},"content":{"rendered":"

\u7b2c\u4e00\u6b65：HEP\u4ecb\u7ecd<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u5fae\u5149\u56fe\u50cf\u589e\u5f3a\u65b9\u6cd5\u901a\u5e38\u9700\u8981\u5927\u91cf\u7684\u6210\u5bf9\u8bad\u7ec3\u6570\u636e，\u8fd9\u5728\u73b0\u5b9e\u4e16\u754c\u4e2d\u662f\u4e0d\u5207\u5b9e\u9645\u7684\u3002\u6700\u8fd1，\u5df2\u7ecf\u63a2\u7d22\u4e86\u65e0\u76d1\u7763\u7684\u65b9\u6cd5\u6765\u6d88\u9664\u5bf9\u6210\u5bf9\u8bad\u7ec3\u6570\u636e\u7684\u4f9d\u8d56\u3002\u7136\u800c，\u7531\u4e8e\u7f3a\u4e4f\u5148\u9a8c，\u5b83\u4eec\u5728\u4e0d\u540c\u7684\u73b0\u5b9e\u4e16\u754c\u573a\u666f\u4e2d\u8868\u73b0\u4e0d\u7a33\u5b9a\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898，\u6587\u7ae0\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u6709\u6548\u5148\u9a8c\u7684\u65e0\u76d1\u7763\u5fae\u5149\u56fe\u50cf\u589e\u5f3a\u65b9\u6cd5，\u79f0\u4e3a\u76f4\u65b9\u56fe\u5747\u8861\u5148\u9a8c（HEP）\u3002\u6587\u7ae0\u7684\u5de5\u4f5c\u662f\u53d7\u5230\u4e86\u4e00\u4e2a\u6709\u8da3\u7684\u89c2\u5bdf\u7ed3\u679c\u7684\u542f\u53d1，\u5373\u76f4\u65b9\u56fe\u5747\u8861\u589e\u5f3a\u56fe\u50cf\u7684\u7279\u5f81\u56fe\u548c\u5730\u9762\u5b9e\u51b5\u662f\u76f8\u4f3c\u7684\u3002\u5177\u4f53\u800c\u8a00，\u6587\u7ae0\u5236\u5b9aHEP\u4ee5\u63d0\u4f9b\u4e30\u5bcc\u7684\u7eb9\u7406\u548c\u4eae\u5ea6\u4fe1\u606f\u3002\u5b83\u5d4c\u5165\u5230\u53d1\u5149\u6a21\u5757（LUM）\u4e2d，\u6709\u52a9\u4e8e\u5c06\u4f4e\u5149\u56fe\u50cf\u5206\u89e3\u4e3a\u7167\u5ea6\u56fe\u548c\u53cd\u5c04\u7387\u56fe，\u53cd\u5c04\u7387\u56fe\u53ef\u4ee5\u88ab\u89c6\u4e3a\u6062\u590d\u7684\u56fe\u50cf\u3002\u7136\u800c，\u57fa\u4e8eRetinex\u7406\u8bba\u7684\u63a8\u5bfc\u8868\u660e，\u53cd\u5c04\u7387\u56fe\u53d7\u5230\u4e86\u566a\u58f0\u7684\u6c61\u67d3\u3002\u6587\u7ae0\u5f15\u5165\u4e86\u4e00\u79cd\u566a\u58f0\u53bb\u7ea0\u7f20\u6a21\u5757（NDM），\u5728\u672a\u914d\u5bf9\u5e72\u51c0\u56fe\u50cf\u7684\u53ef\u9760\u5e2e\u52a9\u4e0b，\u53bb\u7ea0\u7f20\u53cd\u5c04\u56fe\u4e2d\u7684\u566a\u58f0\u548c\u5185\u5bb9\u3002\u5728\u76f4\u65b9\u56fe\u5747\u8861\u5148\u9a8c\u548c\u566a\u58f0\u89e3\u7ea0\u7f20\u7684\u6307\u5bfc\u4e0b，\u6211\u4eec\u7684\u65b9\u6cd5\u53ef\u4ee5\u6062\u590d\u66f4\u7cbe\u7ec6\u7684\u7ec6\u8282，\u5e76\u4e14\u80fd\u591f\u5728\u73b0\u5b9e\u4e16\u754c\u7684\u4f4e\u5149\u573a\u666f\u4e2d\u6291\u5236\u566a\u58f0\u3002\u5927\u91cf\u7684\u5b9e\u9a8c\u8868\u660e，\u6211\u4eec\u7684\u65b9\u6cd5\u4e0e\u6700\u5148\u8fdb\u7684\u65e0\u76d1\u7763\u5fae\u5149\u589e\u5f3a\u7b97\u6cd5\u76f8\u6bd4\u8868\u73b0\u826f\u597d，\u751a\u81f3\u4e0e\u6700\u65b0\u7684\u6709\u76d1\u7763\u7b97\u6cd5\u76f8\u5339\u914d\u3002<\/p>\n<\/p>\n

\u7b2c\u4e8c\u6b65：HEP\u7f51\u7edc\u7ed3\u6784<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u7b97\u6cd5\u6574\u4f53\u6d41\u7a0b\u5982\u4e0b\u56fe，\u7ed3\u5408\u63d0\u4eae\u548c\u964d\u566a\u3002<\/p>\n

\"\"<\/p>\n<\/p>\n

\u7b2c\u4e09\u6b65：\u6a21\u578b\u4ee3\u7801\u5c55\u793a<\/p>\n

from abc import ABC
\nfrom thop import profile
\nfrom torch import nn
\nimport torch
\nimport argparse
\nfrom utils import get_config
\nfrom models.NDM_model import Conv2dBlock
\ntry:
\n from itertools import izip as zip
\nexcept ImportError:
\n pass<\/p>\n

parser = argparse.ArgumentParser()
\nparser.add_argument('–denoise_config', type=str, default='.\/configs\/unit_NDM.yaml', help="denoise net configuration")
\nparser.add_argument('–light_config', type=str, default='configs\/unit_LUM.yaml', help='Path to the config file.')
\nparser.add_argument('–input_folder', type=str, default='.\/test_images', help="input image path")
\nparser.add_argument('–output_folder', type=str, default='.\/NDM_results', help="output image path")
\nparser.add_argument('–denoise_checkpoint', type=str, default='.\/checkpoints\/NDM_LOL.pt', help="checkpoint of denoise")
\nparser.add_argument('–light_checkpoint', type=str, default='.\/checkpoints\/LUM_LOL.pth', help="checkpoint of light")
\nopts = parser.parse_args()<\/p>\n

class DecomNet(nn.Module, ABC):
\n def __init__(self, params):
\n super(DecomNet, self).__init__()
\n self.norm = params['norm']
\n self.activ = params['activ']
\n self.pad_type = params['pad_type']
\n #
\n self.conv0 = Conv2dBlock(4, 32, 3, 1, 1, norm=self.norm, activation=self.activ, pad_type=self.pad_type)
\n self.conv1 = Conv2dBlock(4, 64, 9, 1, 4, norm=self.norm, activation='none', pad_type=self.pad_type)
\n self.conv2 = Conv2dBlock(64, 64, 3, 1, 1, norm=self.norm, activation=self.activ, pad_type=self.pad_type)
\n self.conv3 = Conv2dBlock(64, 128, 3, 2, 1, norm=self.norm, activation=self.activ, pad_type=self.pad_type)
\n self.conv4 = Conv2dBlock(128, 128, 3, 1, 1, norm=self.norm, activation=self.activ, pad_type=self.pad_type)
\n self.conv5 = nn.ConvTranspose2d(128, 64, 3, 2, 1, 1)
\n self.activation = nn.ReLU(inplace=True)
\n self.conv6 = Conv2dBlock(128, 64, 3, 1, 1, norm=self.norm, activation=self.activ, pad_type=self.pad_type)
\n self.conv7 = Conv2dBlock(96, 64, 3, 1, 1, norm=self.norm, activation='none', pad_type=self.pad_type)
\n self.conv8 = Conv2dBlock(64, 4, 3, 1, 1, norm=self.norm, activation='none', pad_type=self.pad_type)<\/p>\n

def forward(self, input_im):
\n input_max = torch.max(input_im, dim=1, keepdim=True)[0]
\n image = torch.cat((input_max, input_im), dim=1)
\n # Refelectance
\n x0 = self.conv0(image)
\n # print('x0:', x0.shape)
\n x1 = self.conv1(image)
\n # print('x1:', x1.shape)
\n x2 = self.conv2(x1)
\n # print('x2:', x2.shape)
\n x3 = self.conv3(x2)
\n # print('x3:', x3.shape)
\n x4 = self.conv4(x3)
\n # print('x4:', x4.shape)
\n x5 = self.conv5(x4)
\n x5 = self.activation(x5)
\n # print('x5:', x5.shape)
\n cat5 = torch.cat((x5, x2), dim=1)
\n x6 = self.conv6(cat5)
\n # print('x6:', x6.shape)
\n cat6 = torch.cat((x6, x0), dim=1)
\n x7 = self.conv7(cat6)
\n # print('x7:', x7.shape)
\n x8 = self.conv8(x7)
\n # print('x8:', x8.shape)
\n # Outputs
\n R = torch.sigmoid(x8[:, 0:3, :, :])
\n L = torch.sigmoid(x8[:, 3:4, :, :])
\n return R, L
\nif __name__ == "__main__":
\n light_config = get_config(opts.light_config)
\n model = DecomNet(light_config)
\n input = torch.randn(1, 3, 256, 256)
\n flops, params = profile(model, inputs=(input, ))
\n print("flops:{}".format(flops))
\n print("params:{}".format(params)) <\/p>\n

\u7b2c\u56db\u6b65：\u8fd0\u884c<\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n<\/p>\n

\u7b2c\u4e94\u6b65：\u6574\u4e2a\u5de5\u7a0b\u7684\u5185\u5bb9<\/p>\n

\"\"<\/p>\n

\u9879\u76ee\u5b8c\u6574\u6587\u4ef6\u4e0b\u8f7d\u8bf7\u89c1\u6f14\u793a\u4e0e\u4ecb\u7ecd\u89c6\u9891\u7684\u7b80\u4ecb\u5904\u7ed9\u51fa：\u27b7\u27b7\u27b7<\/p>\n

https:\/\/www.bilibili.com\/video\/BV1CUB2YgEPo\/<\/p>\n

\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

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