1. Deep screen image crop and enhance
Week 3
(Aaron Ott, Amir Mazaheri)

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

3. Cropper Uses a frozen VGG-19 model to get feature map
Applies convolutions, normalizations, and activations
Final dense layer creates 6-number theta value for affine transformation
STN takes input image and applies affine transformation

4. Enhancer Pretrained EDSR (trained on DIV2K) Modified form of Resnet
Pretrained EDSR (trained on DIV2K)
Modified form of Resnet
Uses modified residual block, which excludes batch normalization and final ReLU layer
16 Residual blocks
Subpixel Conv2D layers for upscaling the image
Scales the image 4x
Lim, Son, Kim, Nah, Lee. “Enhanced Deep Residual Networks for Single Image Super-Resolution”. 10 July 2017

5. Combined Cropper and Enhancer
Trained with 2 outputs and 2 Loss Functions:
- Trained Cropper on VGG + Cosine Proximity (Inception Loss)
- Trained Enhancer on VGG + MSE

6. Results Metric\Model Cropper Cropper & Enhancer PSNR 11.1903 16.2060
SSIM
0.4254
0.4909
MSE
0.0796
0.0281
MOS
2.6143
2.8857
Results
Cropper & Enhancer
Input
Cropper
Actual

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

8. Synthetic Dataset
Problem: There is no existing dataset to use when solving this problem, and taking pictures takes too much time
Solution: Automatically generate images with various transformations over various backgrounds
- Current problems: sometimes image edges get cut out, difficult to get full variety of possible images, doesn’t yet account for discoloration or image noise, dataset only includes birds

9. Synthetic Dataset Results
Cropper +
Enhancer
Metric\Model
Cropper + Enhancer
Cropper
PSNR
SSIM
0.4882
0.5421
MSE
0.0262
0.0212
Input
Cropper
Truth

10. What’s Next Short Term (next week)
Train cropper on harder synthetic dataset
Expand synthetic dataset to include multiple image classes
Develop GAN to train model further
Long Term (to the end of the summer)
Connect model to solve existing issues: identification/classification
Try new enhancers and enhancement techniques