Study on the Infrared Image Enhancement Method Based on Multi-Scale Fusion

Abstract

Infrared thermal imaging technology has been widely applied across multiple sectors of modern society. Nevertheless, constrained by its imaging mechanism and the variability of environmental conditions during imaging, infrared images often suffer from poor quality, characterized by low resolution, weak contrast, blurred edges, missing detail information, and low signal-to-noise ratio. Consequently, enhancing infrared images to boost their quality is essential for facilitating subsequent observation and practical use. In response to this issue, this paper proposes an image enhancement framework integrating brightness adjustment, detail sharpening, and multi-scale fusion, which effectively addresses the common problems of infrared images such as insufficient brightness, low contrast, and blurred details. Initially, the light-map of the original image is extracted and estimated based on the Retinex model, followed by gamma correction to generate the first input for fusion. To offset the detail loss in overexposed regions caused by gamma correction, a sharpened version of the original image is introduced as the second fusion input. Subsequently, weight maps for the two inputs are computed, aggregated, and normalized. Finally, the enhanced image is output through a multi-scale fusion pyramid model. Experiments conducted on MATLAB demonstrate that the enhanced images achieve balanced brightness, along with clear edges and detailed textures.