Infrared and Visible Image Fusion Using Truncated Huber Penalty Function Smoothing and Visual Saliency Based Threshold Optimization

Chaowei Duan,Yiliu Liu,Changda Xing,Zhisheng Wang

doi:10.3390/electronics11010033

Chaowei Duan, Yiliu Liu + Show 2 more

Open Access

https://doi.org/10.3390/electronics11010033

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Abstract

An efficient method for the infrared and visible image fusion is presented using truncated Huber penalty function smoothing and visual saliency based threshold optimization. The method merges complementary information from multimodality source images into a more informative composite image in two-scale domain, in which the significant objects/regions are highlighted and rich feature information is preserved. Firstly, source images are decomposed into two-scale image representations, namely, the approximate and residual layers, using truncated Huber penalty function smoothing. Benefiting from the edge- and structure-preserving characteristics, the significant objects and regions in the source images are effectively extracted without halo artifacts around the edges. Secondly, a visual saliency based threshold optimization fusion rule is designed to fuse the approximate layers aiming to highlight the salient targets in infrared images and remain the high-intensity regions in visible images. The sparse representation based fusion rule is adopted to fuse the residual layers with the goal of acquiring rich detail texture information. Finally, combining the fused approximate and residual layers reconstructs the fused image with more natural visual effects. Sufficient experimental results demonstrate that the proposed method can achieve comparable or superior performances compared with several state-of-the-art fusion methods in visual results and objective assessments.

Highlights

Infrared (IR) and visible image fusion is a current research hot spot in image processing because of its numerous applications in computer vision tasks [1], such as military reconnaissance, biological recognition, target detecting, and tracking, etc
This paper proposes a novel image fusion method for infrared and visible images using truncated Huber penalty function (THPF) smoothing based image decomposition, visual saliency based threshold optimization (VSTO) fusion strategy, and sparse representation (SR) fusion strategy
In the presented THPF-VSTO-SR method, source images are decomposed into the approximate layer images and residual layer images

Summary

Introduction

Infrared (IR) and visible image fusion is a current research hot spot in image processing because of its numerous applications in computer vision tasks [1], such as military reconnaissance, biological recognition, target detecting, and tracking, etc. Infrared images can expose the thermal radiation difference of different objects, which identify the targets from the poor lighting backgrounds. IR images usually have low definition backgrounds and poor texture details. Visible imaging is able to capture the reflected light of an object and provide considerably high resolution and texture details; it is often affected by bad weather [2,3]. To obtain the plenty information for the exact understanding of a scene, most users often have to analyze the multimodality images of a scene one-by-one. Analyzing each individual member of the multimodality images of a scene usually requires numerous resources such as more people, more time, and more money.

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