Deep Image Enhancement Project Progress Report

The Deep Screen Image Crop and Enhance project, led by Aaron Ott and Amir Mazaheri, focuses on improving image quality through a multi-step approach involving image detection, cropping, and enhancement. The project utilizes advanced techniques like super-resolution networks and deep residual networks to enhance image quality. Progress metrics show significant improvements in image quality metrics like PSNR and SSIM. Challenges and future plans include refining GAN architecture and exploring different single image super-resolution methods.

• Deep Learning
• Image Processing
• Super-Resolution
• Enhancement
• Project Progress

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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 https://github.com/krasserm/super-resolution - 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. Metrics: Metrics: Last Week (Cropper) PSNR: 11.1903 Cropper using a Spatial Transformation Network SSIM: 0.4254 MSE: 0.0796 Input Output Actual

6. Metrics: Metrics: This Week (Cropper + Enhancer) PSNR: 15.8368 SSIM: 0.4807 MSE: 0.0316 Input Output Actual

7. Metrics Compared Model Model \ \ Metric Metric PSNR PSNR SSIM SSIM MSE MSE Cropper 11.1903 0.4254 0.0796 Cropper + Enhancer 15.8368 15.8368 0.4807 0.4807 0.0316 0.0316

8. Shortcomings of PSNR, SSIM, and MSE Model Model \ \ Metric Metric PSNR PSNR SSIM SSIM MSE MSE Cropper 11.1903 0.4254 0.0796 Cropper + Enhancer 15.8368 0.4807 0.0316 Cropper + Enhancer w/ MSE Loss 18.2626 18.2626 0.5314 0.5314 0.0166 0.0166

9. Shortcomings of PSNR, SSIM, and MSE Metrics: Metrics: PSNR: 18.2626 Input SSIM: 0.5314 MSE: 0.0166 Output Loss Functions VGG + Cosine Proximity MSE Actual

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