CFA Bayer image sequence denoising and demosaicking chain

TL;DR

This paper introduces a novel imaging pipeline that combines Bayer CFA denoising with demosaicking for image sequences, effectively reducing noise and artifacts while preserving image quality.

Contribution

It presents a new spatio-temporal patch-based algorithm for joint denoising and demosaicking, addressing a gap in existing literature.

Findings

Superior noise reduction and artifact avoidance demonstrated on real examples

Effective preservation of image details and color fidelity

Outperforms traditional sequential processing methods

Abstract

The demosaicking provokes the spatial and color correlation of noise, which is afterwards enhanced by the imaging pipeline. The correct removal previous or simultaneously with the demosaicking process is not usually considered in the literature. We present a novel imaging chain including a denoising of the Bayer CFA and a demosaicking method for image sequences. The proposed algorithm uses a spatio-temporal patch method for the noise removal and demosaicking of the CFA. The experimentation, including real examples, illustrates the superior performance of the proposed chain, avoiding the creation of artifacts and colored spots in the final image.