Restoring images degraded by adverse weather remains challenging due to spatially heterogeneous degradations. Many existing weather-specific restoration models rely on weather-agnostic global aggregation, naive cross-scale fusion, and deterministic objectives, which struggle to handle heterogeneous degradations in all-in-one adverse-weather settings. To address these limitations, we propose an Uncertainty-guided Adverse-weather Restoration Network (UAR-Net), a weather-specific AiO framework that integrates a gated transformer with balanced multi-scale skip connections. Specifically, we employ Gated Dual-scale Transformer Blocks (GDTB) to jointly model selective global interactions and multi-scale local structures, a progressive Balanced Multi-scale Skip Connection (BMSC) for balanced multi-scale feature integration, and an Uncertainty-Aware Refinement Head (URH) that performs artifact removal, detail enhancement, and predictive uncertainty estimation. The model is supervised by a Brightness-Aware Energy Loss (BAE-Loss) to encourage accurate reconstruction with well-calibrated uncertainty. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple adverse-weather benchmarks. The codes will open source upon acceptance.
Zhe-Ke Jin, Yuning Cui, Tianhu Jin et al.· 0 citations
All-in-one image restoration has recently attracted considerable attention for its ability to address multiple degradation types within a single, unified framework. However, existing methods often incur substantial computational overhead, especially when incorporating explicit degradation priors via complex auxiliary branches, hindering their practical deployment. In this paper, we propose AdaptIR, an efficient all-in-one image restoration network equipped with adaptive frequency enhancement. Recognizing that different degradations impact distinct frequency subbands and exhibit spatially varying restoration demands, we design an Adaptive Frequency Enhancement Module (AFEM) that couples frequency learning with adaptive convolutions to better capture frequency-aware information. Specifically, AFEM learns pixel-wise adaptive attention weights to modulate the spectra of dynamic convolutions, enabling spatially adaptive and content-aware restoration. Furthermore, we introduce a lightweight backbone featuring a Receptive Field Expansion Module (RFEM), which enlarges the receptive field of a convolutional U-shaped architecture by convolving wavelet-transform coefficients. By integrating the plug-and-play AFEM into the bottleneck of the baseline model, AdaptIR achieves state-of-the-art performance on all-in-one image restoration tasks involving multiple degradations, while maintaining high computational efficiency. Moreover, the proposed model can be readily extended to single-degradation tasks (e.g., dehazing, desnowing, and deraining) and domain-specific applications, including ultra-high-definition (UHD), medical, and remote sensing image restoration.
Real-world paired image dehazing remains challenging because haze degradation is spatially non-uniform, illumination-dependent, and physically ambiguous even when haze-free references are available. Existing end-to-end restoration networks usually learn a deterministic mapping from a hazy observation to a clean target, while degradation-sensitive feature responses, reverse haze-formation consistency, and cross-domain negative structure remain insufficiently exploited. In this paper, we propose Backbone-Agnostic Stochastic Perturbation Learning (BSPL), a plug-and-play framework for end-to-end real-world image dehazing. BSPL first introduces a Learnable Stochastic Perturbation Modulator (LSPM), which learns input-conditioned channel-wise and spatial-wise perturbation distributions and converts the resulting feature-response discrepancies into adaptive modulation weights. It then develops a Prior-informed Perturbation-guided Reconstruction Module (PPRM), which reuses the learned bottleneck perturbations together with transmission and atmospheric-light priors to reconstruct the hazy observation from the restored result and enforce degradation consistency. Furthermore, we propose a Dual-space Domain-diversified Distribution-aware Contrastive Loss ($D^3$CL) to regularize both clean restoration and hazy reconstruction spaces with real-world and synthetic negatives. Experiments on five real-world paired benchmarks show that BSPL consistently improves multiple representative backbones with only marginal additional inference overhead.
Bingcai Wei, Yuning Cui, Mingyu Liu et al.· 0 citations
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