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Restoring old images using deep learning. The architecture entails a DnCNN model comprised of 17-Layers of a homogenous CNN with batch normalization and ReLU. Initial code is adopted from Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising by K. Zhang, et. al By: Maya Hussein & Nada Badawi

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Deep_Learning_Image_Restoration

Restoring old images using deep learning. The architecture entails a DnCNN model comprised of 17-Layers of a homogenous CNN with batch normalization and ReLU. Initial code is adopted from Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising by K. Zhang, et. al

About

Restoring old images using deep learning. The architecture entails a DnCNN model comprised of 17-Layers of a homogenous CNN with batch normalization and ReLU. Initial code is adopted from Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising by K. Zhang, et. al By: Maya Hussein & Nada Badawi

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