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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).
- Underwater scene prior inspired deep underwater image and video enhancement1,262
An underwater image enhancement convolutional neural network model based on underwater scene prior based on underwater scene prior is proposed, called UWCNN, which directly reconstructs the clear latent underwater image, which benefits from the underwater scene prior which can be used to synthesize underwater image training data.
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- 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.
- 891
- 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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- LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement515
A trainable Convolutional Neural Network is proposed for weakly illuminated image enhancement, namely LightenNet, which takes a weakly illumination image as input and outputs its illumination map that is subsequently used to obtain the enhanced image based on Retinex model.
- 439
- Towards Robust Blind Face Restoration with Codebook Lookup Transformer434
This paper demonstrates that a learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration mapping by casting blind face restoration as a code prediction task, while providing rich visual atoms for generating high-quality faces.
- 407
- Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images355
An end-to-end Dense Attention Fluid Network (DAFNet) for SOD in optical RSIs that significantly outperforms the existing state-of-the-art SOD competitors is proposed and a new and challenging optical RSI dataset is constructed that contains 2,000 images with pixel-wise saliency annotations.
- 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.
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- Diving deeper into underwater image enhancement: A survey315
The underwater image formation models are introduced, which are the base of training data synthesis and design of deep networks, and also helpful for understanding the process of underwater image degradation and the robustness and effectiveness of deep algorithms.
- ProPainter: Improving Propagation and Transformer for Video Inpainting267
This work introduces dual-domain propagation that combines the advantages of image and feature warping, exploiting global correspondences reliably, and proposes a mask-guided sparse video Transformer, which achieves high efficiency by discarding unnecessary and redundant tokens.
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- LEDNet: Joint Low-Light Enhancement and Deblurring in the Dark258
This work introduces a novel data synthesis pipeline that models realistic low-light blurring degradations and presents the first large-scale dataset, LOL-Blur, and an effective network, named LEDNet, to perform joint low-light enhancement and deblurring.
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- 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.
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- CIR-Net: Cross-Modality Interaction and Refinement for RGB-D Salient Object Detection231
A convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement is presented, which outperforms the state-of-the-art saliency detectors both qualitatively and quantitatively.
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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