Existing Photo Editing Tools
Input
“Auto Enhance” on iPhone
“Auto Tone” in Lightroom
Ours
Previous Work
Retinex-based Methods • LIME: [TIP 17] • WVM: [CVPR 16] • JieP: [ICCV 17] Learning-based Methods • HDRNet: [SIGGRAPH 17] • White-Box: [ACM TOG 18] • Distort-and-Recover: [CVPR 18] • DPE: [CVPR 18]
演示者 2019-05-08 03:51:55
-------------------------------------------The target of video super-solutionis to increase the resolution of videos with rich details. [click] It is an old and fundamental problemthat has been studied since several decades ago. [click] Video SR enables many applications,such as High-definition video generation from low-res sources. [click]
• Illumination maps for natural images typically have relatively simple forms with known priors.
• The model enables customizing the enhancement results by formulating constraints on the illumination.
Our result
Expert-retouched
More Results
Input
JieP
HDRNet
DPE
White-box
Distort-and-Recover
Our result
Expert-retouched
More Results
Input
JieP
HDRNet
DPE
White-box
Distort-and-Recover
Our result
Expert-retouched
More Results
Input
WVM
JieP
HDRNet
DPE
White-Box
Distort-and-Recover
Our result
More Results
Input
WVM
JieP
HDRNet
DPE
White-Box
Distort-and-Recover
Our result
More Results
Input
iPhone
Lightroom
Our result
More Results
Input
iPhone
Lightroom
Our result
2. 视频超分辨 率
Motivation
Old and Fundamental Several decades ago [Huang et al, 1984] → near recent Many Applications HD video generation from low-res sources
33
演示者 2019-05-08 03:51:55
-------------------------------------------[click] Video enhancement with details. In this example, characters on the roof and textures of the tree in SR result are much clearer then input. [click]
Ours
PSNR 28.61 24.66 23.69 28.41 28.81 29.41 30.71 30.80
SSIM 0.866 0.850 0.701 0.841 0.867 0.871 0.884 0.893
Visual Comparison: Our Dataset
Input
JieP
HDRNet
Image SR Traditional: [Freeman et al, 2002], [Glasner et al, 2009], [Yang et al, 2010], etc. CNN-based: SRCNN [Dong et al, 2014], VDSR [Kim et al, 2016], FSRCNN [Dong et al, 2016], etc.
Quantitative Comparison: Our Dataset
Method HDRNet
DPE White-Box Distort-and-Recover Ours w/o ������������������������, w/o ������������������������,w/o ������������������������ Ours with ������������������������, w/o ������������������������, w/o ������������������������ Ours with ������������������������, with ������������������������, w/o ������������������������
Input
Our result
More Results
Input
JieP
HDRNet
DPE
White-box
Distort-and-Recover
Our result
Expert-retouched
More Results
Input
JieP
HDRNet
DPE
White-box
Distort-and-Recover
More Comparison Results: User Study
Input
WVM
JieP
HDRNet
DPE
White-Box
Distort-and-Recover
Our result
Limitaion
演示者 2019-05-08 03:51:53
-------------------------------------------Our work also exists some limitations,the first limitation is the region is almost black without any trace of texture. We can see the top two images. The second limitation is our method doen’t clear noise in the enhanced result.
Motivation
Old and Fundamental Several decades ago [Huang et al, 1984] → near recent Many Applications HD video generation from low-res sources Video enhancement with details Text/object recognition in surveillance videos
34
演示者 2019-05-08 03:51:55
-------------------------------------------[click] And also, it can benefit text or object recognition in low-quality surveillance videos. In this example, numbers on the car become recognizable only in the super-resolved result.
DPE White-Box Distort-and-Recover Ours w/o ������������������������, w/o ������������������������, w/o ������������������������ Ours with ������������������������, w/o ������������������������, w/o ������������������������ Ours with ������������������������, with ������������������������, w/o ������������������������
Limitations of Previous Methods
Input
WVM [CVPR’16]
JieP [ICCV’17]
HDRNet [Siggraph’17]
DPE [CVPR’18]
White-Box [TOG’18]
Distort-and-Recover [CVPR’18]
Ours
Why This Model?
Ours
PSNR 26.33 23.58 21.69 24.54 27.02 28.97 30.03 30.97