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

🖼️ Image Restoration¶

🤖 AAAI2026 · 10 paper notes

📌 Same area in other venues: 📷 CVPR2026 (107) · 🔬 ICLR2026 (61) · 🧪 ICML2026 (21) · 🧠 NeurIPS2025 (26) · 📹 ICCV2025 (31)

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

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.
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.
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.
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.
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.
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.
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.