MCMS: Multi-Category Information and Multi-Scale Stripe Attention for Blind Motion Deblurring

TL;DR

This paper introduces MCMS, a three-stage deep learning model that leverages multi-category information and multi-scale stripe attention to effectively enhance blind motion deblurring by fusing high-frequency edge details and low-frequency structural features.

Contribution

It proposes a novel three-stage encoder-decoder network with grouped feature fusion and a multi-scale stripe attention mechanism for improved motion deblurring.

Findings

Outperforms recent methods on various datasets

Effectively fuses high-frequency and low-frequency features

Enhances feature representation with multi-scale stripe attention

Abstract

Deep learning-based motion deblurring techniques have advanced significantly in recent years. This class of techniques, however, does not carefully examine the inherent flaws in blurry images. For instance, low edge and structural information are traits of blurry images. The high-frequency component of blurry images is edge information, and the low-frequency component is structure information. A blind motion deblurring network (MCMS) based on multi-category information and multi-scale stripe attention mechanism is proposed. Given the respective characteristics of the high-frequency and low-frequency components, a three-stage encoder-decoder model is designed. Specifically, the first stage focuses on extracting the features of the high-frequency component, the second stage concentrates on extracting the features of the low-frequency component, and the third stage integrates the extracted low-frequency component features, the extracted high-frequency component features, and the original blurred image in order to recover the final clear image. As a result, the model effectively improves motion deblurring by fusing the edge information of the high-frequency component and the structural information of the low-frequency component. In addition, a grouped feature fusion technique is developed so as to achieve richer, more three-dimensional and comprehensive utilization of various types of features at a deep level. Next, a multi-scale stripe attention mechanism (MSSA) is designed, which effectively combines the anisotropy and multi-scale information of the image, a move that significantly enhances the capability of the deep model in feature representation. Large-scale comparative studies on various datasets show that the strategy in this paper works better than the recently published measures.