1. Deep screen image crop and enhance
Week 2
(Aaron Ott, Amir Mazaheri)

2. Problem
We have taken a photo of an image, and we want the original image.
This can be broken into 2 parts:
Image Detector/Cropper
Image Enhancer

3. Enhancer - Pretrained EDSR (trained on DIV2K)
- Pretrained EDSR (trained on DIV2K)
- Modified form of Resnet
- Actually scales up the image 4x
Lim, Son, Kim, Nah, Lee. “Enhanced Deep Residual Networks for Single Image Super-Resolution”. 10 July 2017

4. Combining the Cropper and Enhancer
Had to use lambda functions and AveragePooling2D to get the Enhancer to properly work with the Cropper
Loss Functions:
- Trained Cropper on VGG+CosineProximity
- Trained Enhancer on VGG+MSE

5. Last Week (Cropper) Metrics: PSNR: 11.1903 SSIM: 0.4254
MSE:
Last Week (Cropper)
Cropper using a Spatial Transformation Network
Input
Output
Actual

6. This Week (Cropper + Enhancer)
Metrics:
PSNR:
SSIM:
MSE:
This Week (Cropper + Enhancer)
Input
Output
Actual

7. Metrics Compared Model \ Metric PSNR SSIM MSE Cropper 11.1903 0.4254
0.0796
Cropper + Enhancer
0.4807
0.0316

8. Shortcomings of PSNR, SSIM, and MSE
Model \ Metric
PSNR
SSIM
MSE
Cropper
0.4254
0.0796
Cropper + Enhancer
0.4807
0.0316
Cropper + Enhancer w/ MSE Loss
0.5314
0.0166

9. Shortcomings of PSNR, SSIM, and MSE
Metrics:
PSNR:
SSIM:
MSE:
Input
Output
Loss Functions
VGG + Cosine Proximity
MSE
Actual

10. Next Week Create and refine GAN architecture
Refine EDSR or try a different SISR (WDSR)