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- Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement2,430
A novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network and shows that it generalizes well to diverse lighting conditions.
- An Underwater Image Enhancement Benchmark Dataset and Beyond2,389
This paper constructs an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images and proposes an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs).
- Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation915
A novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network and demonstrates the advantages of the method over state-of-the-art methods qualitatively and quantitatively.
- Low-Light Image and Video Enhancement Using Deep Learning: A Survey651
This paper proposes a low-light image and video dataset, in which the images and videos are taken by different mobile phones’ cameras under diverse illumination conditions, and provides a unified online platform that covers many popular LLIE methods.
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- PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators351
This paper proposes a physical model-guided GAN model for UIE, referred to as PUGAN, and designs the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results.
- RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors241
This work rethink the degradation of real hazy images and proposes a phenomenological pipeline considering diverse degradation types, which drastically mitigates the domain gap between synthetic and real data, and recalculates the distance when matching the features to the HQPs by a controllable matching operation, which facilitates finding better counterparts.
- Towards An End-to-End Framework for Flow-Guided Video Inpainting234
An End-to-End framework for Flow-Guided Video Inpainting (E2 FGVI) through elaborately designed three trainable modules, namely, flow completion, feature propagation, and content hallucination modules that can be Jointly optimized, leading to a more efficient and effective inpainting process.
- AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation229
This work presents All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation based on two essential designs that derive multiple groups of fine-grained flow fields from one pair of updated coarse flows for performing backward warping on the input frames separately.
- A hybrid method for underwater image correction223
Subjective and objective performance evaluations demonstrate that the proposed method significantly improves both color and visibility of degraded underwater images, and is comparable to and even outperforms several state-of-the-art methods.
- Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement222
This work proposes a new solution, UHDFour, that embeds Fourier transform into a cascaded network and contributes the first real UHD LLIE dataset, \textbf{UHD-LL}, that contains 2,150 low-noise/normal-clear 4K image pairs with diverse darkness and noise levels captured in different scenarios.
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- A parallel down-up fusion network for salient object detection in optical remote sensing images108
A novel Parallel Down-up Fusion network (PDF-Net) for SOD in optical RSIs, which takes full advantage of the in-path low- and high-level features and cross-path multi-resolution features to distinguish diversely scaled salient objects and suppress the cluttered backgrounds.
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- 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.
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- DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution68
This work proposes the DiT4SR, one of the pioneering works to tame the large-scale DiT model for Real-ISR, which endow the DiT model with superior performance in Real-ISR, which is demonstrated by extensive experiments.
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- LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation67
This study designs a first-frame-conditioned pipeline that uses an off-the-shelf text-to-image model for content generation so that the authors' tuned video diffusion model mainly focuses on motion learning, and develops an effective inference trick, shared-noise sampling, which can improve the stability of videos with computational costs.
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- Iterative Predictor-Critic Code Decoding for Real-World Image Dehazing53
A novel Iterative Predictor-Critic Code Decoding framework for real-world image dehazing, abbreviated as IPC-Dehaze, which leverages the high-quality codebook prior encapsulated in a pre-trained VQGAN to improve code prediction accuracy and ensure stable dehazing performance.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; 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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