🖼️ Image Restoration

🧠 NeurIPS2026 · 4 paper notes

📌 Same area in other venues: 🎞️ ECCV2026 (73) · 📷 CVPR2026 (135) · 🔬 ICLR2026 (61) · 🧪 ICML2026 (21) · 🤖 AAAI2026 (10) · 🧠 NeurIPS2025 (26)

🔥 Top topics: Image Restoration ×2

Beyond Spatial-Domain Supervision: A Relation Constrained Space for Multi-Modal Image Fusion

Rather than treating source images as ideal fused images, RCS-Fusion learns a relational supervision space from frozen DINO/CLIP features and trains different fusion backbones with shared, complementary, and conflict-coordination constraints; its Transformer variant achieves 0.730 mAP in detection using fused M3FD images.

i-DEQ: A stable inertial deep equilibrium model for image restoration

i-DEQ integrates restarted inertial optimization into training and inference for an explicit-energy deep equilibrium model, achieving reconstruction quality close to ELDER with substantially shorter equilibrium-solving time in small image restoration experiments, but its theory establishes conditional stationarity complexity and practical training is not always stable.

Learning Where and What to Restore for Composite Image Restoration

CART allocates restoration compute through window-level spatial routing inside a U-Net, then controls image-level channel routing with task features shaped by multi-hot degradation supervision, achieving 29.95 dB average PSNR on CDD-11 while trading its speed advantage for more parameters and higher peak memory than MoCE-IR.

Multidimensional Observer Model and Perceptual Dimensions of Human Image Quality Assessment

The paper represents images as Gaussian distributions in a low-dimensional perceptual space and fits human choices through noisy candidate–reference distance comparisons; the CORnet-S Multi observer reaches 95% of its own above-chance performance at 3, 9, and 96 dimensions on BAPPS, PieAPP, and NIGHTS, respectively, suggesting task-dependent perceptual structures.