DEEP SCREEN IMAGE CROP AND ENHANCE Week 1

DEEP SCREEN IMAGE CROP AND ENHANCE Week 1

DEEP SCREEN IMAGE CROP AND ENHANCE Week 1 (Aaron Ott, Amir Mazaheri) 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 and Image Enhancer

Spatial Transformer Networks Jaderberg, Simonyan, Zisserman, Korayk. Spatial Transformer Networks, 4 Feb 2016 - Localization Network generate Theta - Grid Generator Maps pixels for transformation - Sampler Creates transformed image https://github.com/oarriaga/STN.kera

Spatial Transformer Networks (cont.) Benefits - Localization network is fully trainable - Module can be included in any existing model Uses:

- reshaping input image to better focus on important regions - attention mechanism - cropping images? https://github.com/oarriaga/STN.kera Dataset

10 Images (100 Samples, 90 training 10 validation) - (Caltech-UCSD Birds 200) Loss Function Results Validation Loss: 0.1195 => 0.05349 over 200 epochs (less than 5 minutes of training)

Output Input Output Ground Truth Image Enhancement: Existing Research

Photo Enhancement Super Resolution SRGAN EDSR WDSR https://github.com/krasserm/super-resolution.git Chen, Wang, Kao, Chuang. Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs, CVPR 2018 Ledig, Theis, Huszar, Caballero, Cunningham, Acosta, Aitken, Tejani, Totz, Wang, Shi. Photo-Realistic Single Image Super-Resolution Using a Generative

Adversarial Network. 25 May 2017 Lim, Son, Kim, Nah, Lee. Enhanced Deep Residual Networks for Single Image Super-Resolution. 10 July 2017 Next Steps Fine-tune the photo cropper Test different localization network hyperparameters Loss function and optimizer Build a more difficult data set to test the cropper on Validate on new images

Further distance away More background noise Change location within image Begin working on Image Enhancement Connect Cropper to existing image enhancement network Fine tune image enhancement networks

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