ECCV2026 Image Restoration AI paper notes paper summaries Super-Resolution Multimodal/VLM Navigation Adversarial Robustness Personalized Generation

🖼️ Image Restoration¶

🎞️ ECCV2026 · 13 paper notes

📌 Same area in other venues: 📷 CVPR2026 (135) · 🔬 ICLR2026 (61) · 🧪 ICML2026 (21) · 🤖 AAAI2026 (10) · 🧠 NeurIPS2025 (26) · 📹 ICCV2025 (31)

🔥 Top topics: Image Restoration ×5 · Super-Resolution ×4

340 FPS Reflection-free Video from Spikes Modulated by a Rapidly Rotating Polarizer: A rapidly rotating polarizer modulates spike-camera measurements, and reliability-weighted physical separation followed by residual refinement reconstructs reflection-free video at a nominal 340 FPS output cadence, achieving 27.0598 dB PSNR on the authors' synthetic test set.
CogSENet: Blind Image Deblurring with Blur-Conditioned Semantic Routing and Explicit Frequency Fusion: CogSENet reformulates blind image deblurring from a passive pixel regression into an active, semantically aligned reconstruction process. By mimicking the active visual scanning, retinal functional differentiation, and focal adaptation of the eagle visual system, it proposes three core designs: a Semantic-Driven State Space Module (SDSSM), a Bi-Frequency Fusion Block (BFFB), and joint modulation based on a Continuous Blur Field (CBF) and CLIP semantics. With only 8.9M parameters, it outperforms state-of-the-art (SOTA) methods such as EVSSM and FFTformer on the GoPro, HIDE, and RealBlur datasets.
Fabric Image Demoiréing Benchmark from Synthesis to Restoration: This paper presents the first systematic study on the fabric image demoiréing problem. It introduces PRISM, a physical imaging chain simulation-based residual injection synthesis framework (including the first fabric moiré benchmark with 16,050 image pairs), and FaDeNet, a conservative restoration network specifically designed for the spectrally entangled characteristics of fabrics, significantly outperforming existing screen demoiréing methods in terms of PSNR, SSIM, and LPIPS.
Fidelity- and Perception-Aware Local Implicit Attention for Arbitrary-Scale Image Super-Resolution: FPLIA proposes a dual-stream framework that fuses the fidelity features of a regression backbone and the perceptual features of a diffusion model via asymmetric bidirectional cross-attention (FPAM) and pixel-wise adaptive selection (FPSM), simultaneously achieving high fidelity and high perceptual quality in ASISR.
FlowDec: Temporal Conditional Flow Decorruptor for Robust Continuous Vision-Language Navigation: FlowDec proposes an image decorruption framework based on conditional flow matching (CFM). By incorporating a hybrid temporal conditioning strategy and action centroid-guided filtering, it enhances robustness against diverse visual corruptions without modifying the downstream VLN backbone. It improves the relative navigation Success Rate (SR) of continuous VLN agents by 25.33% on R2R-CE and 9.38% on RxR-CE, while operating 3 to 8 times faster during inference compared to diffusion-based TTA baselines.
FMA-Net++: Motion- and Exposure-Aware Joint Video Super-Resolution and Deblurring: FMA-Net++ proposes a non-recurrent sequence-level framework based on Hierarchical Bidirectional Aggregation (HRBA) blocks. By introducing an Exposure Time-aware Modulation (ETM) module to inject frame-level exposure information into features, and combining it with an exposure-aware Flow-Guided Dynamic Filtering (FGDF) to jointly model motion- and exposure-varying degradations, the method achieves SOTA accuracy and inference speed on the VSRDB task.
FreeMEF: A Flexible Multi-Exposure Fusion Transformer for Arbitrary Number of Frames: FreeMEF proposes a "There and Back Again" two-stage paradigm: it first recursively aggregates multi-exposure features from an arbitrary number of frames into a global representation using a Recurrent State Space Module, and then restores the reference frame guided by an Extreme-Aware Hybrid Attention. This enables flexible inference on 2/3/5 frames while significantly suppressing ghosting artifacts and enhancing dynamic range.
FreqOrtho-SR: Frequency-Guided Orthogonal Expert Learning for Real-World Image Super-Resolution: FreqOrtho-SR proposes two core modules: Frequency-Guided LoRA Mixture-of-Experts (FreqMoE) and Orthogonal Gradient Projection (OGP). Driven by adaptive expert routing based on FFT degradation features and orthogonal constraints on pixel-semantic subspaces, it achieves degradation-adaptive real-world image super-resolution within a single-step diffusion model framework, obtaining optimal or sub-optimal trade-offs between fidelity and perceptual quality across multiple benchmarks.
LogicIR: Logic Gate Networks for Image Restoration: LogicIR is the first logic gate network (LGN) specifically designed for image restoration. Through a fully logic-gate UNet architecture, a differentiable bit decoding layer, and an Index Shuffling cross-group communication mechanism, it achieves competitive restoration quality on denoising, deblocking, and deraining tasks with operations (BOPs) far lower than BNN and LUT methods, demonstrating the feasibility of pure logic gate operations in image restoration.
Personalization as Inverse Planning: Learning Latent Design Intents for Agentic Slide Generation via Structural Denoising: Spire models page-level presentation slide personalization (PSP) as an inverse planning problem. By applying discrete structural perturbations to golden slides, it constructs self-supervised "denoising" signals to train Critic and Planner agents (7B-level) to cooperatively and iteratively refine design specifications under RL. Without relying on specific executors, it infers the user's implicit design intent and significantly outperforms strong GPT-based baselines in both visual similarity and VLM-as-Judge scores.
Spectral and Trajectory Regularization for Diffusion Transformer Super-Resolution: StrSR proposes a joint framework of asymmetric adversarial distillation and frequency-domain distribution matching. By replacing the DiT discriminator with a CLIP-ConvNeXt discriminator, it avoids model collapse, and uses a Frequency Distribution Loss (FDL) to suppress grid-like periodic artifacts inherent in DiT. It achieves high-quality single-step real-world image super-resolution on DiT architectures for the first time.
TaskTok: Delving into Task Tokens for Task-driven Image Restoration: TaskTok proposes a task-driven image restoration (TDIR) framework. It leverages the property of 1D tokenizers where tokens are specialized by index position to encode different visual attributes. Through a learnable token switch and a lightweight token refiner, TaskTok selectively restores only the token subset most relevant to downstream tasks (classification, segmentation, or detection), substantially improving downstream task performance while drastically reducing computational overhead.
UHD-MFF: Shattering Barriers in Multi-Focus Ultra-High-Definition Image Fusion via Learnable Lookup Tables: This paper proposes the first 4K ultra-high-definition multi-focus image fusion dataset, UHD-MFF (1,950 pairs of 3840×2160 images), and designs a scale-decoupled learnable lookup table framework, UMF-LUT. It uses a low-resolution C-LUT for coarse region-level decision-making and a high-resolution D-LUT for edge-level refinement. With only 0.008M parameters, it achieves state-of-the-art fusion quality while enabling real-time 4K inference at 90fps, allowing deployment on mobile devices.