Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks

TL;DR

This paper introduces a novel deep learning architecture for joint image demosaicking and denoising, inspired by classical regularization and convex optimization, achieving superior results with fewer parameters.

Contribution

A transparent, interpretable deep network architecture for joint demosaicking-denoising, outperforming previous methods and requiring fewer trainable parameters.

Findings

Outperforms previous approaches on noisy and noise-free data

Requires fewer trainable parameters than state-of-the-art methods

Generalizes well even with small training datasets

Abstract

Demosaicking and denoising are among the most crucial steps of modern digital camera pipelines and their joint treatment is a highly ill-posed inverse problem where at-least two-thirds of the information are missing and the rest are corrupted by noise. This poses a great challenge in obtaining meaningful reconstructions and a special care for the efficient treatment of the problem is required. While there are several machine learning approaches that have been recently introduced to deal with joint image demosaicking-denoising, in this work we propose a novel deep learning architecture which is inspired by powerful classical image regularization methods and large-scale convex optimization techniques. Consequently, our derived network is more transparent and has a clear interpretation compared to alternative competitive deep learning approaches. Our extensive experiments demonstrate that our network outperforms any previous approaches on both noisy and noise-free data. This improvement in reconstruction quality is attributed to the principled way we design our network architecture, which also requires fewer trainable parameters than the current state-of-the-art deep network solution. Finally, we show that our network has the ability to generalize well even when it is trained on small datasets, while keeping the overall number of trainable parameters low.