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- Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring432
An efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain is developed.
- Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution279
This work proposes a spatially-adaptive feature modulation (SAFM) mechanism for efficient SR design that is 3× smaller than state-of-the-art efficient SR methods, e.g., IMDN, and yields comparable performance with much less memory usage.
- Physics-Based Generative Adversarial Models for Image Restoration and Beyond188
An algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, and image deraining) by generative models with adversarial learning within the GAN framework.
- Physics-Based Feature Dehazing Networks184
An effective feature dehazing unit (FDU), which is applied to the deep feature space to explore useful features for image dehazed based on the physics model, and can effectively dehaze images with favorable performance against state-of-the-art methods.
- Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining165
An end-to-end multi-scale Transformer that leverages the potentially useful features in various scales to facilitate high-quality image reconstruction and performs favorably against the state-of-the-art ones on both synthetic and real-world benchmark datasets is developed.
- DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring150
The proposed deep Wiener deconvolution network facilitates deblurred results with visibly fewer artifacts and quantitatively outperforms state-of-the-art non-blind image deblurring methods by a wide margin.
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- PromptRestorer: A Prompting Image Restoration Method with Degradation Perception111
A prompting degradation perception modulator is proposed, which adequately considers the characters of the self-attention mechanism and pixel-wise modulation, to better perceive the degradation priors from global and local perspectives.
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- Efficient Visual State Space Model for Image Deblurring92
This paper proposes a simple yet effective visual state space model (EVSSM) for image deblurring, leveraging the benefits of state space models (SSMs) for visual data, and proposes an efficient discriminative frequency domain-based feedforward network (EDFFN), which can effectively estimate useful frequency information for latent clear image restoration.
- The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report91
This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes, and gauge the state-of-the-art in efficient single-image super-resolution.
- DLGSANet: Lightweight Dynamic Local and Global Self-Attention Network for Image Super-Resolution83
A sparse global self-attention (SparseGSA) module is developed to select the most useful similarity values so that the most useful global features can be better utilized for image reconstruction.
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- Towards Unified Deep Image Deraining: A Survey and a New Benchmark72
An online platform is built to provide the off-the-shelf toolkit, involving the large-scale performance evaluation of existing image deraining methods, and a new high-quality benchmark named HQ-RAIN is constructed to conduct extensive evaluations.
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- FoundIR: Unleashing Million-Scale Training Data to Advance Foundation Models for Image Restoration54
A diffusion-based generalist model is utilized to remove degradations by learning the degradation-agnostic common representations from diverse inputs, where an incremental learning strategy is adopted to better guide model training.
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- Learning Data Terms for Non-blind Deblurring46
This work proposes a simple and effective discriminative framework to learn data terms that can adaptively handle blurred images in the presence of severe noise and outliers and shows that the shrinkage functions learned at the intermediate stages can effectively suppress noise and preserve image structures.
- FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution44
This work proposes a simple and effective method, named FaithDiff, to fully harness the impressive power of latent diffusion models (LDMs) for faithful image SR, and develops an effective alignment module to explore useful features from degraded inputs to align well with the diffusion process.
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- The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report39
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques.
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- NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results37
This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images to establish a new and powerful benchmark for the task of removing raindrops under varying lighting and focus conditions.
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- Subspace segmentation by dense block and sparse representation33
This work proposes a novel dense block and sparse representation (DBSR) for subspace segmentation and provides experimental results for synthetic and benchmark data showing that this method can outperform the state-of-the-art.
- Blur kernel estimation via salient edges and low rank prior for blind image deblurring30
It is found that the blur process changes the similarity of neighboring image patches and based on the intriguing observation, it is shown how to effectively apply the low rank prior to blind image deblurring and a new algorithm is presented that combines low rankPrior and salient edge selection.
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- The Fourth Challenge on Image Super-Resolution ($\times$4) at NTIRE 2026: Benchmark Results and Method Overview24
The NTIRE 2026 image super-resolution ($\times$4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026, provides a unified benchmark and offers insights into current progress and future directions in image super-resolution.
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- NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results22
The submitted methods achieved strong performance on the KwaiVIR benchmark, demonstrating encouraging progress in short-form UGC video restoration in the wild.
- NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results21
An overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images demonstrates the growing progress in this challenging task.
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- The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview20
A review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the resulting outcomes and offering an in-depth overview of the latest trends in the field.
- Deep Unpaired Blind Image Super-Resolution Using Self-supervised Learning and Exemplar Distillation16
An exemplar distillation module is developed that is able to explore useful information from exemplar HR images to constrain the deep models learned from the self-supervised learning module for the final HR image restoration.
- DeblurDiff: Real-World Image Deblurring with Generative Diffusion Models14
The proposed Latent Kernel Prediction Network (LKPN) outperforms state-of-the-art image deblurring methods on both benchmark and real-world images and iterative refinement enhances the accuracy and robustness of the deblurring process.
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- FoundIR-v2: Optimizing Pre-Training Data Mixtures for Image Restoration Foundation Model10
This work proposes a high-capacity diffusion-based image restoration foundation model, FoundIR-v2, which adopts a data equilibrium scheduling paradigm to dynamically optimize the proportions of mixed training datasets from different tasks, and introduces an effective Mixture-of-Experts (MoE)-driven scheduler into generative pre-training.
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- LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results8
A detailed analysis of the submitted methods and corresponding results is provided, emphasizing recent progress in unified real-world image restoration and establishing a benchmark for future research in real-world low-level vision.
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- Bayesian Low-Rank and Sparse Nonlinear Representation for Manifold Clustering8
This paper proposes Bayesian low-rank and sparse non linear representation (BLSN) model exploiting nonlinear representation, which can be solved by traditional algorithm in Bayesian statistics easily without knowing the explicit mapping by kernel trick.
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- UniRain: Unified Image Deraining with RAG-based Dataset Distillation and Multi-objective Reweighted Optimization6
UniRain is proposed, an effective unified image deraining framework capable of restoring images degraded by rain streak and raindrop under both daytime and nighttime conditions and is incorporated into the asymmetric mixture-of-experts (MoE) architecture to facilitate consistent performance and improve robustness across diverse scenes.
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- Subspace Learning Based Low-Rank Representation6
After extending the linear projection to nonlinear mapping, this method can handle manifold clustering problem which is a general case of subspace segmentation and can also be applied in the problem of recognition by adding suitable penalty on the learned subspace.
- Sparse Gradient Pursuit for Robust Visual Analysis6
This paper proposes a novel criterion, named Sparse Gradient Pursuit (SGP), to simultaneously preserve the within-class homogeneity and the between-class discrimination for unsupervised data clustering and shows that the proposed SGP criterion is generic and can be extended to handle semi-supervised learning problems by incorporating the label information into the data graph.
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- Bridging Fidelity-Reality with Controllable One-Step Diffusion for Image Super-Resolution5
CODSR is proposed, a controllable one-step diffusion network for image super-resolution that leverages original uncompressed information from LQ inputs to provide high-fidelity conditioning for the diffusion process and develops a region-adaptive generative prior activation method to effectively enhance perceptual richness without sacrificing local structural fidelity.
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- AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results3
This paper thoroughly evaluates the state-of-the-art advances in high-FPS single image motion deblurring, showcasing the significant progress in the field, while leveraging samples of the novel dataset, MIORe, that introduces challenging examples of movement patterns.
- LUT-GCE: Lookup Table Global Curve Estimation for Fast Low-light Image Enhancement3
This work develops a novel cubic curve formulation for light enhancement, which enables an image-adaptive and pixel-independent curve for the range adjustment of an image and proposes a global curve estimation network (GCENet), a very light network with only 25.4k parameters.
- Collaborative Feedback Discriminative Propagation for Video Super-Resolution2
This work develops a discriminative alignment correction (DAC) method and proposes a collaborative feedback propagation (CFP) module based on feedback and gating mechanisms to explore spatial and temporal information of different timestep features from forward and backward propagation simultaneously.
- The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results1
A review of the second LoViF Challenge on Real-World All-in-One Image Restoration provides a comprehensive analysis of the submitted solutions and corresponding results, highlighting recent advances in real-world all-in-one image restoration.
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- Cascaded Temporal Updating Network for Efficient Video Super-Resolution1
This work proposes a cascaded temporal updating network (CTUN) for efficient VSR, and develops an implicit cascaded alignment module to explore spatio-temporal correspondences from adjacent frames and a unidirectional propagation updating network to efficiently explore long-range temporal information, which is crucial for high-quality video reconstruction.
- ExpRDiff: Short-exposure Guided Diffusion Model for Realistic Local Motion Deblurring1
A blur-aware guided image restoration method that utilizes sharp structural details from short-exposure images, facilitating accurate reconstruction of heavily blurred regions and a short-exposure guided diffusion model that explores useful features from short-exposure images and blurred regions to better constrain the diffusion process.
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- PGP-DiffSR: Phase-Guided Progressive Pruning for Efficient Diffusion-based Image Super-Resolution–
A lightweight diffusion method, PGP-DiffSR, is developed by removing redundant information from diffusion models under the guidance of the phase information of inputs for efficient image super-resolution and significantly reducing computational load and memory consumption.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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