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- Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement2,451
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.
- 924
- LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement506
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.
- 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.
- A hybrid method for underwater image correction220
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.
- 193
- Passive forensics for copy-move image forgery using a method based on DCT and SVD183
The proposed method can effectively detect multiple copy-move forgery and precisely locate the duplicated regions, even when an image was distorted by Gaussian blurring, AWGN, JPEG compression and their mixed operations.
- 170
- A Cascaded Convolutional Neural Network for Single Image Dehazing105
Qualitatively and quantitatively experimental results demonstrate that the proposed method effectively removes haze from such images, and outperforms several state-of-the-art dehazing methods.
- 105
- Is Underwater Image Enhancement All Object Detectors Need?101
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.
- 99
- Underwater image enhancement by dehazing and color correction96
The enhanced images, as a result of implementing the proposed approach, are characterized by relatively genuine color, increased contrast and brightness, reduced noise level, and better visibility.
- Transductive Zero-Shot Learning With a Self-Training Dictionary Approach91
A bidirectional mapping-based semantic relationship modeling scheme that seeks for cross-modal knowledge transfer by simultaneously projecting the image features and label embeddings into a common latent space is proposed.
- Cross-Domain Correlation Distillation for Unsupervised Domain Adaptation in Nighttime Semantic Segmentation89
This work proposes a novel domain adaptation framework via cross-domain correlation distillation, called CCDistill, which achieves the state-of-the-art performance for nighttime semantic segmentation and is a one-stage domain adaptation network which can avoid affecting the inference time.
- Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning88
A novel stacked semantics-guided attention (S2GA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions.
- Image denoising using modified Perona–Malik model based on directional Laplacian81
A modified Perona-Malik (MPM) model based on directional Laplacian, which diffuses image along the edge direction of the original image, is proposed, which can alleviate the staircasing effect, preserve sharp discontinuities, and remove noise simultaneously.
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- Zero-Shot Learning via Latent Space Encoding76
Extensive experimental results on four benchmark datasets clearly demonstrate the superiority of the proposed encoder–decoder approach, namely latent space encoding (LSE), on several ZSL tasks, including traditional ZSL, generalized Z SL, and zero-shot retrieval.
- Single underwater image enhancement based on color cast removal and visibility restoration50
Experimental results demonstrate that the proposed underwater image-enhancement method can effectively remove color cast, improve contrast and visibility, and recover natural appearance of degraded underwater images.
- Conditional mutual information-based feature selection algorithm for maximal relevance minimal redundancy47
A feature selection algorithm based on conditional mutual information for maximal relevance minimal redundancy (CMI-MRMR) is proposed and compared with several feature selection algorithms to validate the performance and show that it can achieve better feature selection performance.
- Transductive Zero-Shot Learning With Adaptive Structural Embedding45
Two corresponding methods named Adaptive STructural Embedding (ASTE) and Self-PAced Selective Strategy (SPASS) for visual-semantic embedding and domain adaptation in cross-modality learning and unseen class prediction steps are presented.
- Manifold regularized cross-modal embedding for zero-shot learning37
A Manifold regularized Cross-Modal Embedding (MCME) approach for ZSL is proposed by formulating the manifold constraint for intrinsic structure of the visual features as well as aligning pairwise consistency in order to preserve the locally visual structure in the embedding process.
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- TIENet: task-oriented image enhancement network for degraded object detection32
A task-oriented image enhancement network (TIENet) to directly improve degraded object detection’s performance by enhancing the degraded images by presenting a fast Fourier transform-based structure loss for the enhancement task.
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 2026-10-14. 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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