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Works22 from public data
- UIEC^2-Net: CNN-based underwater image enhancement using two color space405
This method is the first to use HSV color space for underwater image enhancement based on deep learning and efficiently and effectively integrate both RGB Color Space and HSV Color Space in one single CNN.
- Is Underwater Image Enhancement All Object Detectors Need?102
This study uses 18 state-of-the-art underwater image enhancement algorithms, covering traditional, CNN-based, and GAN-based algorithms, to preprocess underwater object detection data and retrain seven popular deep learning-based object detectors using the corresponding results enhanced by different algorithms, obtaining 126 underwater object detection models.
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- Salient object detection in low-light RGB-T scene via spatial-frequency cues mining14
This work proposes an RGB-T SOD model by mining spatial-frequency cues by mining spatial-frequency cues, called SFMNet, for low-light scenes, and can achieve higher accuracy than the existing models for low-light scenes.
- Transformer guidance dual-stream network for salient object detection in optical remote sensing images12
A transformer guidance dual-stream network (TGDNet) is proposed for SOD in optical RSIs inspired by the long-range dependencies of transformer to extract multi-scale features by global receptive fields and separately refine them according to the characteristics of feature hierarchies.
- Illumination-Guided progressive unsupervised domain adaptation for low-light instance segmentation11
An Illumination-Guided Progressive Unsupervised Domain Adaptation method, called IPULIS, for low-light instance segmentation by progressively exploring the alignment of features at image-, instance, and pixel-levels between normal- and low-light conditions under illumination guidance.
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- PaIaNet: position-aware and identification-aware network for low-light salient object detection5
A position-aware and identification-aware network (PaIaNet) for SOD, inspired by the hunting mechanism of predators in biology, that performs favorably from comparisons of qualitative and quantitative evaluations against other state-of-the-art methods in SOD of low-light images, and even achieves competitive performance when extended to normal-light scenes.
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- MB-DAMPNet: a novel multi-branch denoising-based approximate message passing algorithm via deep neural network for image reconstruction–
A novel multi-branch denoising-based approximate message passing algorithm via deep neural network, dubbed MB-DAMPNet, which significantly outperforms other state-of-the-art methods in image reconstruction accuracy.
- A NEW NON-CONVEX APPROACH FOR COMPRESSIVE SENSING MRI–
A novel approach, dubbed as regularized maximum entropy function (RMEF) minimization algorithm, to approximate L q -norm (0 < q < 1) as sparsity promoting objectives and then the regularization mechanism for improving the de-noising performance is adopted.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. 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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