Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images

TL;DR

This paper introduces GCP-Net, a deep learning model that leverages the green channel prior to jointly denoise and demosaic burst images, improving reconstruction quality in real-world noisy conditions.

Contribution

The paper proposes a novel GCP-Net that uses green channel prior information to enhance joint denoising and demosaicking for real-world burst images, addressing limitations of existing methods.

Findings

GCP-Net outperforms existing methods in preserving image details.

The model effectively reduces noise in real-world burst images.

Experimental results show significant improvements in image quality.

Abstract

Denoising and demosaicking are essential yet correlated steps to reconstruct a full color image from the raw color filter array (CFA) data. By learning a deep convolutional neural network (CNN), significant progress has been achieved to perform denoising and demosaicking jointly. However, most existing CNN-based joint denoising and demosaicking (JDD) methods work on a single image while assuming additive white Gaussian noise, which limits their performance on real-world applications. In this work, we study the JDD problem for real-world burst images, namely JDD-B. Considering the fact that the green channel has twice the sampling rate and better quality than the red and blue channels in CFA raw data, we propose to use this green channel prior (GCP) to build a GCP-Net for the JDD-B task. In GCP-Net, the GCP features extracted from green channels are utilized to guide the feature extraction and feature upsampling of the whole image. To compensate for the shift between frames, the offset is also estimated from GCP features to reduce the impact of noise. Our GCP-Net can preserve more image structures and details than other JDD methods while removing noise. Experiments on synthetic and real-world noisy images demonstrate the effectiveness of GCP-Net quantitatively and qualitatively.