PQGAL-Net: Perceptual Quality Guided Generative Adversarial Learning for Non-uniform Illumination Underwater Image Enhancement

Digital Signal Processing, Journal Year: 2025, Volume and Issue: unknown, P. 105048 - 105048

Published: Feb. 1, 2025

Language: Английский

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MUFFNet: lightweight dynamic underwater image enhancement network based on multi-scale frequency

Frontiers in Marine Science, Journal Year: 2025, Volume and Issue: 12

Published: Feb. 11, 2025

Introduction The advancement of Underwater Human-Robot Interaction technology has significantly driven marine exploration, conservation, and resource utilization. However, challenges persist due to the limitations underwater robots equipped with basic cameras, which struggle handle complex environments. This leads blurry images, severely hindering performance automated systems. Methods We propose MUFFNet, an image enhancement network leveraging multi-scale frequency analysis address challenge. introduces a frequency-domain-based convolutional attention mechanism extract spatial information effectively. A Multi-Scale Enhancement Prior algorithm enhances high-frequency low-frequency features while Information Flow module mitigates stratification blockage. Joint Loss framework facilitates dynamic optimization. Results Experimental results demonstrate that MUFFNet outperforms existing state-of-the-art models consuming fewer computational resources aligning enhanced images more closely human visual perception. Discussion generated by exhibit better alignment perception, making it promising solution for improving robotic vision

Language: Английский

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Underwater Image Enhancement Method Based on Contrast Stretching and Lab Color Space Correction

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 94 - 104

Published: Jan. 1, 2025

Language: Английский

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Multi-scale cascaded attention network for underwater image enhancement

Frontiers in Marine Science, Journal Year: 2025, Volume and Issue: 12

Published: March 11, 2025

The complexity of underwater environments combined with light attenuation and scattering in water often leads to quality degradation images, including color distortion blurred details. To eliminate obstacles imaging, we propose an image enhancement method based on a cascaded attention network called MSCA-Net. Specifically, this designs attention-guided module that connects channel pixel both serial parallel ways simultaneously achieve feature refinement representation enhancement. Afterward, multi-scale integration capture information details at different scales within the image. Meanwhile, residual connections are introduced assist deep learning via acquiring more detailed from shallow features. We conducted extensive experiments various datasets, results demonstrate our still holds advantage when compared latest methods.

Language: Английский

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MSFFNet: Multi-stream feature fusion network for underwater image enhancement

Displays, Journal Year: 2025, Volume and Issue: unknown, P. 103023 - 103023

Published: March 1, 2025

Language: Английский

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