1. Deep screen image crop and enhance
Week 8
(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. 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

8. Synthetic Dataset Results
Original Cropper + Enhancer
Cropper w/ SD
Cropper w/ 2 SDs
Original Cropper
Input
Truth
* Note: Used separate validation data set that none of the networks had been trained on.
PSNR
SSIM
0.3366
0.3335
0.3450
0.3437
MSE
0.0609
0.0586
0.0578

9. Projective Transformation Issues
It turns out the STN we were using cannot handle projective transformations (it doesn’t take in account a z axis in any of the equations)
After searching through many implementations, we could not find a STN implementation that allowed for projective transformations.
Existing projective transformation functions don’t allow for passing gradients.
Workarounds?

10. New Objective:
Can we give our network an input image with multiple images, tell it which class of image to retrieve, and retrieve the correct image?
1 - Balloon
2 - Birdhouse
4 – Persian Cat
3 - French Bulldog

11. Other additions to our network:
Attention module – Identify area of the photo where the image specified is
Multiple Croppers – Try to progressively crop the image to get better and better crops

12. Next Week
Continue running experiments
Get paper written