Texture and noise dual adaptation for infrared image super-resolution

Abstract

Recent efforts have explored leveraging visible light images to enrich texture details in infrared (IR) super-resolution. However, this direct adaptation approach often becomes a double-edged sword, as it improves texture at the cost of introducing noise and blurring artifacts. Such imperfections are inherent in the spatial domain of visible images and are accentuated during the imaging process. Enhancing IR image quality by integrating rich texture details from visible images, while minimizing noise transfer, presents a challenging research avenue. To address these challenges, we propose the Texture and Noise Dual Adaptation SRGAN (DASRGAN), an innovative framework specifically engineered for robust IR super-resolution model adaptation. DASRGAN operates on the synergy of two key components: (1) Texture-Oriented Adaptation (TOA) to refine texture details meticulously, and (2) Noise-Oriented Adaptation (NOA), dedicated to minimizing noise transfer. Specifically, TOA uniquely integrates a specialized discriminator, incorporating a prior extraction branch, and employs a Sobel-guided adversarial loss to align texture distributions effectively. Concurrently, NOA utilizes a noise adversarial loss to distinctly separate the generative and Gaussian noise pattern distributions during adversarial training. Our extensive experiments confirm DASRGAN’s superiority. Comparative analyses against leading methods across multiple benchmarks and upsampling factors reveal that DASRGAN sets new state-of-the-art performance standards. Code are available at https://github.com/yongsongH/DASRGAN.