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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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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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