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- Underwater Camera: Improving Visual Perception Via Adaptive Dark Pixel Prior and Color Correction231
Experimental results show that the novel method for underwater image restoration outperforms state-of-the-art methods, demonstrating its efficacy in dealing with uneven lighting and diverse underwater environments.
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- HCLR-Net: Hybrid Contrastive Learning Regularization with Locally Randomized Perturbation for Underwater Image Enhancement210
The HCLR-Net, a novel approach built upon a distinctive hybrid contrastive learning regularization strategy that incorporates a unique methodology for constructing negative samples, significantly enhances the visual quality of underwater images, establishing its innovative and practical applicability.
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- SGDNet176
Experimental results on both authentically and synthetically distorted IQA datasets demonstrate the superiority of the SGDNet, as compared to the state-of-the-art approaches.
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- A Dilated Inception Network for Visual Saliency Prediction158
This work proposes an end-to-end dilated inception network (DINet) for visual saliency prediction that captures multi-scale contextual features effectively with very limited extra parameters and improves the performance of the saliency model by using a set of linear normalization-based probability distribution distance metrics as loss functions.
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- No-Reference Quality Assessment for 360-Degree Images by Analysis of Multifrequency Information and Local-Global Naturalness101
This model is the first no-reference quality assessment method for 360-degree images that combines multifrequency information and image naturalness and outperforms state-of-the-art full-reference (FR) and NR approaches.
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- Progressive Self-Guided Loss for Salient Object Detection96
It is demonstrated that this new loss function can guide the SOD model to highlight more complete salient objects step-by-step and meanwhile help to uncover the spatial dependencies of the salient object pixels in a region growing manner.
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- Underwater Image Enhancement With Cascaded Contrastive Learning84
The proposed CCL-Net involves two cascaded stages, i.e., a color correction stage tailored to the color deviation issue and a haze removal stage tailored to improve the visibility and contrast of underwater images, and applies contrastive loss as an additional constraint to guide the training of each stage.
- Blind quality assessment for image superresolution using deep two-stream convolutional networks82
Experimental results on three publicly available SR image quality databases demonstrate the effectiveness and generalization ability of the proposed DeepSRQ method compared with state-of-the-art image quality assessment algorithms.
- A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels79
A new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels, and a new pipeline framework with three sophisticated training strategies is designed.
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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