Multi-scale Fusion of Stretched Infrared and Visible Images

Abstract

Infrared (IR) band sensors can capture digital images under challenging conditions, such as haze, smoke, and fog, while visible (VIS) band sensors seize abundant texture information. It is desired to fuse IR and VIS images to generate a more informative image. In this paper, a novel multi-scale IR and VIS images fusion algorithm is proposed to integrate information from both the images into the fused image and preserve the color of the VIS image. A content-adaptive gamma correction is first introduced to stretch the IR images by using one of the simplest edge-preserving filters, which alleviates excessive luminance shifts and color distortions in the fused images. New contrast and exposedness measures are then introduced for the stretched IR and VIS images to achieve weight matrices that are more in line with their characteristics. The IR and luminance components of the VIS image in grayscale or RGB space are fused by using the Gaussian and Laplacian pyramids. The RGB components of the VIS image are finally expanded to generate the fused image if necessary. Comparisons experimentally demonstrate the effectiveness of the proposed algorithm to 10 different state-of-the-art fusion algorithms in terms of computational cost and quality of the fused images.

Keywords

• multi-scale fusion
• infrared image
• visible image
• content-adaptive gamma correction
• weight measures