2019年深度学习下的图像视频处理技术.pdf

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1、深度学习下的图像视频处理技术优图X-Lab视觉AI负责人专家研究员看得更清，看得更懂目录1.夜景增强2.图像视频去模糊3.视频超分辨率1.夜景图像增强Taking photos is easyAmateur photographers typically create underexposed photosPhoto Enhancement is requiredImage EnhancementInput“Auto Enhance”on iPhone“Auto Tone”in LightroomOursExisting Photo Editing ToolsRetinex-based Met

2、hods LIME:TIP 17 WVM:CVPR 16 JieP:ICCV 17Learning-based Methods HDRNet:SIGGRAPH 17 White-Box:ACM TOG 18 Distort-and-Recover:CVPR 18 DPE:CVPR 18Previous WorkInputWVM CVPR16JieP ICCV17HDRNet Siggraph17DPE CVPR18White-Box TOG18Distort-and-Recover CVPR18OursLimitations of Previous Methods Illumination m

3、aps for natural images typically have relatively simple forms with known priors.The model enables customizing the enhancement results by formulating constraints on the illumination.Why This Model?Advantage:Effective Learning and Efficient LearningNetwork ArchitectureInputNave RegressionExpert-retouc

4、hedAblation StudyMotivation:The benchmark dataset is collected for enhancing general photos instead of underexposed photos,and contains a small number of underexposed images that cover limited lighting conditions.Our DatasetQuantitative Comparison:Our DatasetMethodPSNRSSIMHDRNet26.330.743DPE23.580.7

5、37White-Box21.690.718Distort-and-Recover24.540.712Ours w/o,w/o,w/o 27.020.762Ours with,w/o,w/o 28.970.783Ours with,with,w/o 30.030.822Ours30.970.856MethodPSNRSSIMHDRNet28.610.866DPE24.660.850White-Box23.690.701Distort-and-Recover28.410.841Ours w/o,w/o,w/o 28.810.867Ours with,w/o,w/o 29.410.871Ours w

6、ith,with,w/o 30.710.884Ours30.800.893Quantitative Comparison:MIT-Adobe FiveKVisual Comparison:Our DatasetInputJiePHDRNetDPEWhite-boxDistort-and-RecoverOur resultExpert-retouchedVisual Comparison:MIT-Adobe FiveKInputJiePHDRNetDPEWhite-boxDistort-and-RecoverOur resultExpert-retouchedMore Comparison Re