AAAI2026 Image Restoration AI paper notes paper summaries Super-Resolution Multimodal/VLM Sentiment Analysis Adversarial Robustness Diffusion Models

🖼️ Image Restoration¶

🤖 AAAI2026 · 12 paper notes

📌 Same area in other venues: 🧠 NeurIPS2026 (8) · 🎞️ ECCV2026 (78) · 📷 CVPR2026 (140) · 🔬 ICLR2026 (62) · 🧪 ICML2026 (21) · 🧠 NeurIPS2025 (29)

🔥 Top topics: Image Restoration ×3 · Super-Resolution ×2

Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration: This paper is the first to define and explore the multi-weather nighttime image restoration task. It constructs the AllWeatherNight dataset (8K training + 1K synthetic test + 1K real-world test) and proposes the ClearNight unified framework, which simultaneously removes compound degradations—haze, rain streaks, raindrops, snow, and flare—in a single stage via Retinex dual-prior guidance and weather-aware dynamic specificity–commonality collaboration. With only 2.84M parameters, ClearNight comprehensively surpasses state-of-the-art methods.
Depth-Synergized Mamba Meets Memory Experts for All-Day Image Reflection Separation: This paper proposes DMDNet, which employs a depth-aware scanning strategy (DAScan) to guide Mamba toward salient structures, incorporates a depth-synergized state space model (DS-SSM) to suppress ambiguous feature propagation, and introduces a memory expert compensation module (MECM) to leverage cross-image historical knowledge, achieving all-day (daytime + nighttime) image reflection separation.
MFmamba: A Multi-function Network for Panchromatic Image Resolution Restoration Based on State-Space Model: This paper proposes MFmamba, a multi-function network built upon a UNet++ backbone that integrates a Mamba Upsampling Block (MUB), Dual Pooling Attention (DPA), and a Multi-scale Hybrid Cross Block (MHCB). Using only panchromatic (PAN) images as input, the unified framework simultaneously supports three tasks: super-resolution, spectral restoration, and joint SR with colorization.
ICLR: Inter-Chrominance and Luminance Interaction for Natural Color Restoration in Low-Light Image Enhancement: Targeting two overlooked statistical distribution issues in the HVI color space — large distribution discrepancy between chrominance and luminance branches leading to insufficient complementary feature extraction, and weak inter-chrominance correlation causing gradient conflicts — this paper proposes the ICLR framework. It introduces a Dual-stream Interaction Enhancement Module (DIEM) and a Covariance Correction Loss (CCL) to address these issues from the perspectives of fusion enhancement and statistical distribution optimization, respectively, achieving state-of-the-art performance on the LOL benchmark series.
SpatioTemporal Difference Network for Video Depth Super-Resolution: Motivated by the statistical observation that spatially non-smooth regions and temporally varying regions in video depth super-resolution (VDSR) follow long-tail distributions, this paper proposes STDNet. The method incorporates a spatial difference branch (learning spatial difference representations for intra-frame RGB-D adaptive aggregation) and a temporal difference branch (exploiting temporal difference representations for motion compensation in changing regions). On the TarTanAir dataset at ×16 super-resolution, RMSE is reduced from 112.04 cm to 96.80 cm, outperforming state-of-the-art methods by an average of 27.6%–32.6%.
Temporal Inconsistency Guidance for Super-resolution Video Quality Assessment: This paper proposes TIG-SVQA, a framework that, for the first time, incorporates temporal inconsistency as an explicit guidance signal for super-resolution video quality assessment. The framework introduces an Inconsistency-Highlighted Spatial Module (IHSM) and an Inconsistency-Guided Temporal Module (IGTM), achieving SRCC scores of 0.950, 0.942, and 0.939 on the SFD, MFD, and Combined-VSR datasets, respectively, surpassing all existing IQA/VQA methods.
TMDC: A Two-Stage Modality Denoising and Complementation Framework for Multimodal Sentiment Analysis: This paper proposes TMDC, a two-stage framework in which the first stage learns denoised modality-specific and modality-common representations on complete data, and the second stage leverages denoised representations from available modalities to reconstruct missing ones — marking the first joint treatment of noise and missing modalities in MSA.
Blur-Robust Detection via Feature Restoration: An End-to-End Framework for Prior-Guided Infrared UAV Target Detection: This paper proposes JFD3, an end-to-end dual-branch framework that performs deblurring in the feature domain rather than the image domain, and leverages frequency structure priors to guide the detection network, achieving high-accuracy real-time infrared UAV target detection under motion blur conditions.
SD-PSFNet: Sequential and Dynamic Point Spread Function Network for Image Deraining: SD-PSFNet is a cascaded CNN-based deraining network driven by a dynamic PSF mechanism. It models the optical effects of raindrops via a multi-scale learnable PSF dictionary, combined with a sequential restoration architecture featuring adaptive gated fusion. The method achieves SOTA performance of 33.12 dB on Rain100H and 42.28 dB on RealRain-1k-L, yielding a cumulative gain of 5.04 dB (13.5%) over the baseline MPRNet.
RefiDiff: Progressive Refinement Diffusion for Efficient Missing Data Imputation: RefiDiff proposes a four-stage framework (pre-processing → warm-up → diffusion → polishing) that progressively unifies the predictive and generative imputation paradigms for the first time. Combined with a Mamba-based denoising network, it achieves state-of-the-art performance across 9 datasets while running 4× faster than DIFFPUTER.
CLIPPan: Adapting CLIP as A Supervisor for Unsupervised Pansharpening: This paper proposes CLIPPan, which adapts CLIP via parameter-efficient fine-tuning to understand multispectral/panchromatic/high-resolution multispectral image types and the pansharpening process. It leverages text prompts such as Wald's protocol as semantic supervision signals to achieve unsupervised pansharpening at full resolution without ground truth. It can serve as a plug-and-play module compatible with any pansharpening backbone network.
SmartSplat: Feature-Smart Gaussians for Scalable Compression of Ultra-High-Resolution Images: This paper proposes SmartSplat, a feature-aware 2D Gaussian Splatting image compression framework. By adopting three coordinate strategies—gradient-color-guided variational sampling, repulsive uniform sampling, and scale-adaptive color initialization—it achieves high-quality reconstruction of 8K/16K ultra-high-resolution images under extreme compression ratios (up to 5000×) for the first time.