SRCNN Paper Summary & Implementation

SRCNN proposes a 3 layer CNN for image super-resolution. It is one of the first papers to apply deep neural networks for the task of image super-resolution. The SRCNN architecture is composed of three components: Feature extractor, non-linear mapping, reconstruction. The model is trained to minimize the pixel-wise MSE between the reconstructed and ground truth image. A variety of model architectures and hyperparameter is tested and traded for performance and speed in the paper.