Jul 2026· International Conference on Generative Artificial Intelligence and Image Processing· Vol 14292, pp. 142920L - 142920L-7· 0 citations· 25 references
Engineering
TL;DR
Experimental results show that MPINet effectively removes rain streaks of varying densities while preserving fine textures, and across all evaluated datasets, MPINet outperforms MPRNet by about 6.5% in PSNR and 1.3% in SSIM on average.
Abstract
Image deraining remains a fundamental challenge in computer vision, as rain streaks severely degrade visual quality, obscure scene content, and hinder downstream applications such as autonomous driving and video surveillance. Existing methods often struggle to balance global scene understanding with precise rain removal, resulting in either residual artifacts or over-smoothed textures. To address this issue, we propose MPINet, a Multi-Stage Progressive Image Restoration Network for image deraining. MPINet integrates illumination-aware modeling with global context learning to improve deraining performance under diverse lighting conditions. Specifically, the illumination-aware module generates illumination maps to enhance robustness in scenes with varying brightness, while the UniMetaFormer-based core captures global semantic information through dynamic transformations and attention mechanisms, enabling more effective discrimination between rain streaks and underlying image structures. Built upon the multi-stage restoration paradigm of MPRNet, MPINet adopts a hierarchical progressive framework with patch-based processing and deep supervision across three stages, allowing efficient feature refinement with reasonable model complexity. Experimental results show that MPINet effectively removes rain streaks of varying densities while preserving fine textures. Across all evaluated datasets, MPINet outperforms MPRNet by about 6.5% in PSNR and 1.3% in SSIM on average.
An efficient aligned kernel network (AKNet) is proposed, which innovatively employs super-large convolution kernels to capture global receptive fields with minimal computational overhead, effectively mimicking the long-range dependency modeling of transformers.
Wan Li, Xiao-Lin Zhang· The Visual Computer· 0 citations
Joint demosaicing and denoising is a fundamental step in camera image signal processing, yet remains challenging because different Bayer-like color filter arrays (CFAs) and sensor noise jointly corrupt both color sampling and image content. Existing unified restoration networks explicitly model CFA geometry but are still driven primarily by pixel-level supervision, making them prone to structural degradation around edges, repetitive textures, and moir\'e patterns where local evidence is unreliable. We attribute this limitation partly to the absence of explicit structural guidance beyond pixel-level reconstruction supervision. Motivated by this observation, we propose a structural-guided unified restoration framework that injects pretrained structural knowledge into CFA-aware image restoration. Our model receives a unified five-channel observation consisting of the raw mosaic, CFA masks, and a noise-level map. A SwinIR restoration branch reconstructs pixel details under CFA-conditioned modulation, while a parallel structural reasoning branch extracts complementary structural cues from a sparse pseudo-RGB observation. To bridge the substantial domain gap between sparse noisy sensor data and the natural-image pretraining domain of the structural encoder, we introduce a lightweight trainable adapter before residually fusing structural and restoration features. A shared decoder jointly predicts the restored RGB image and an auxiliary clean mosaic, providing supervision in both image and sensor domains. Extensive experiments across multiple CFA patterns and noise levels demonstrate consistent improvements over state-of-the-art unified and CFA-specific methods, indicating that adapted structural priors can enhance robust camera image restoration. The source codes and dataset are provided in the supplementary material.
Qixin Zheng, Ping Chen, Qiangqiang Shen et al.· 0 citations
The confidence-guided hybrid network (CGHNet) is proposed, a parallel three-branch framework that jointly performs frequency-decoupled local restoration, global context modeling, and pixel-wise degradation prior estimation and its key component is a confidence-guided feature purification mechanism.
Xiaohui Kou, Yang Yan, Qiuyan Wang et al.· Journal of Supercomputing· 0 citations
MGN-AIR is presented, a novel pixel-level restoration framework for all-in-one image restoration that leverages both textual and visual prompts to provide global and local degradation cues, guiding the model on where to look and how to restore at each pixel.
Chun-Xiao Liu, Wei Liu, Anbin Xiong et al.· 0 citations
An adaptive multi-scale decoding framework that effectively balances global context with fine-grained detail is proposed that exhibits superior robustness and generalization across diverse domains, effectively alleviating limitations of existing fusion-based approaches.
