MSD-HENet: Multi-Scale Detail-Preserving Holistic Enhancement Network for Infrared Images

Infrared images, widely utilized in various applications, often suffer from noise, contrast degradation, and detail loss. Existing image enhancement (IE) methods, predominantly designed for the RGB domain, often fail to perform effectively in the infrared domain. This suboptimal performance arises from their inadequate consideration of the intricate interplay between noise and contrast during multi-stage processing, which ultimately results in the loss of fine details. To address these challenges, this paper introduces the Multi-Scale Detail-Preserving Holistic Enhancement Network (MSD-HENet), a framework that leverages a novel interaction mechanism to achieve robust detail preservation while simultaneously denoising and contrast improvement. Specifically, a Detail Information Extractor (DIE) is proposed to effectively extract detail information through multi-scale differential convolution channels during the Deep Denoiser (D²) process, enabling the Contrast Improver (CI) to perform contrast enhancement without losing details, significantly enhancing overall image quality. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art approaches in terms of PSNR, SSIM, and visual effect.