# Image Fusion With Cosparse Analysis Operator

## TL;DR

This paper introduces a novel analysis sparse model for multi-focus image fusion that simultaneously restores and fuses images, outperforming existing methods in quality and robustness.

## Contribution

The paper proposes a new analysis operator learning framework and a fusion function for robust multi-focus image fusion under realistic conditions.

## Key findings

- Outperforms state-of-the-art fusion methods in quality
- Effectively handles noisy and real-world images
- Demonstrates superior visual and quantitative results

## Abstract

The paper addresses the image fusion problem, where multiple images captured with different focus distances are to be combined into a higher quality all-in-focus image. Most current approaches for image fusion strongly rely on the unrealistic noise-free assumption used during the image acquisition, and then yield limited robustness in fusion processing. In our approach, we formulate the multi-focus image fusion problem in terms of an analysis sparse model, and simultaneously perform the restoration and fusion of multi-focus images. Based on this model, we propose an analysis operator learning, and define a novel fusion function to generate an all-in-focus image. Experimental evaluations confirm the effectiveness of the proposed fusion approach both visually and quantitatively, and show that our approach outperforms state-of-the-art fusion methods.