Deep Unfolding for Iterative Stripe Noise Removal

TL;DR

This paper introduces a deep unfolding algorithm that effectively removes stripe noise from infrared images by leveraging neighboring column correlations, improving detail preservation and outperforming existing methods.

Contribution

The paper presents a novel iterative deep unfolding approach that enhances destriping accuracy and efficiency by modeling stripe noise removal as a progressive estimation process.

Findings

Outperforms existing destriping methods in quantitative metrics

Preserves image details more effectively than prior approaches

Demonstrates robustness on artificially corrupted infrared images

Abstract

The non-uniform photoelectric response of infrared imaging systems results in fixed-pattern stripe noise being superimposed on infrared images, which severely reduces image quality. As the applications of degraded infrared images are limited, it is crucial to effectively preserve original details. Existing image destriping methods struggle to concurrently remove all stripe noise artifacts, preserve image details and structures, and balance real-time performance. In this paper we propose a novel algorithm for destriping degraded images, which takes advantage of neighbouring column signal correlation to remove independent column stripe noise. This is achieved through an iterative deep unfolding algorithm where the estimated noise of one network iteration is used as input to the next iteration. This progression substantially reduces the search space of possible function approximations, allowing for efficient training on larger datasets. The proposed method allows for a more precise estimation of stripe noise to preserve scene details more accurately. Extensive experimental results demonstrate that the proposed model outperforms existing destriping methods on artificially corrupted images on both quantitative and qualitative assessments.