DDR-Net: Dual-Stream for Degraded Infrared Image Restoration Network

Seongryeong Lee, Sungho Kim

Abstract: Infrared (IR) imaging technology is drawing significant attention in various critical applications due to its unique capabilities for object detection and scene understanding, even under challenging environments and adverse weather conditions. However, IR imaging fundamentally faces two major limitations: inherent low resolution and contamination by complex noise (Gaussian, stripe, and non-uniformity). Existing approaches suffer from critical limitations. Conventional Super-Resolution (SR) models are vulnerable to composite noise in IR imagery. Existing Denoising (DN) models either address only single noise types or neglect the low-resolution problem. Sequential pipelines (DN $\rightarrow $ SR or SR $\rightarrow $ DN) introduce irreversible information loss, while current joint DN+SR methods consider only simplistic noise models, proving insufficient for real-world IR data. To address these challenges, this study proposes the Dual-stream for Degraded infrared image Restoration Network (DDR-Net), a lightweight end-to-end network for simultaneous complex noise removal and super-resolution. DDR-Net features three key contributions: 1) A novel frequency-domain decomposition-based dual-stream architecture that independently performs noise suppression in low-frequency components and edge preservation in high-frequency components, followed by efficient fusion. 2) The Self-Dual Calibrated Projection Attention (SDCPA) mechanism for effective information exchange between streams. 3) Integration of a validated composite noise model reflecting real-world IR sensor characteristics. Extensive experiments on four benchmark datasets (FLIR, IR100, DLS-NUC-100, and ESPOL FIR) demonstrate that DDR-Net consistently achieves superior performance across various noise levels and upscaling factors ( $\times 2$ , $\times 4$ ). The parameter-efficient architecture ensures practical deployment suitability, providing an effective solution for infrared image restoration in diverse real-world scenarios.

External IDs:doi:10.1109/access.2025.3640134