Quaternion Sparse Decomposition for Multi-focus Color Image Fusion

TL;DR

This paper introduces a novel quaternion domain framework for multi-focus color image fusion, effectively handling complex textures and color details to produce high-quality all-in-focus images.

Contribution

It proposes a quaternion sparse decomposition model and fusion strategies that jointly enhance focus detection and detail preservation in color image fusion.

Findings

Outperforms existing state-of-the-art methods in quality metrics

Effectively preserves color details and textures

Achieves high-precision focus detection

Abstract

Multi-focus color image fusion refers to integrating multiple partially focused color images to create a single all-in-focus color image. However, existing methods struggle with complex real-world scenarios due to limitations in handling color information and intricate textures. To address these challenges, this paper proposes a quaternion multi-focus color image fusion framework to perform high-quality color image fusion completely in the quaternion domain. This framework introduces 1) a quaternion sparse decomposition model to jointly learn fine-scale image details and structure information of color images in an iterative fashion for high-precision focus detection, 2) a quaternion base-detail fusion strategy to individually fuse base-scale and detail-scale results across multiple color images for preserving structure and detail information, and 3) a quaternion structural similarity refinement strategy to adaptively select optimal patches from initial fusion results and obtain the final fused result for preserving fine details and ensuring spatially consistent outputs. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods.