Detail-Aware Network for Infrared Image Enhancement

Infrared (IR) images inherently face the dual challenges of noise contamination and reduced contrast. However, existing image enhancement methods often overlook the intrinsic correlations between these factors - noise and low contrast - during multi-stage enhancement processes. Consequently, this oversight leads to a significant reduction in the fidelity of intricate details within IR images. In this paper, we present a synergistic IR image enhancement network that simultaneously achieves Denoising, Contrast improvement, and Detail preservation (DCDNet), which breaks down the overall enhancement process into more manageable steps. DCDNet is comprised of a Detail Awareness Unit (DAU), a Deep Denoising Prior (DDP), and a Contrast Improvement Module (CIM). To maintain the details within the IR image, DAU is developed to extract the original detail feature information in DDP and integrate them to the CIM during contrast improvement to improve the final result. The detail information is derived from the encoder of the DDP, which focuses on denoising. The preserved detail features are subsequently incorporated into the decoder of the CIM, which is dedicated to enhancing contrast. Experimental results validate that our proposed approach surpasses other state-of-the-art methods for enhancing IR images in terms of PSNR, SSIM, VIF, and performance in downstream tasks. The code and dataset is publicly available at https://github.com/ChickenEating/IR-Enhancement.