A retinex-based enhancing approach for single underwater image

By Xueyang Fu, Peixian Zhuang, Yue Huang, Yinghao Liao, Xiao-Ping Zhang, Xinghao Ding. International Conference on Information Photonics, 2014

563 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c52719dccdd281eac6823012ec65c9c2e1d5b832

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Chongyi Li
2. Chunle Guo
3. Wenhan Yang
4. Chunli Zhu
5. Liheng Bian
6. Lintao Peng
7. Peixian Zhuang
8. Jiaying Liu

In Media Technology

1. Chongyi Li
2. Wenhan Yang
3. Jiaying Liu
4. Chunle Guo
5. Junaed Sattar
6. M. Islam
7. Chunli Zhu
8. Liheng Bian

Citations in the corpus

• Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception (2020, background): “These approaches are inspired by the Retinex theory of human visual perception [37, 72, 23], and mainly focus on restoring background illumination and lightness rendition.”
• Using labeled data to evaluate change detectors in a multivariate streaming environment (2009, unknown): “PIDNet [20] proposes a real-time semantic segmentation network inspired by PID Controller.”
• Underwater Image Enhancement With Hyper-Laplacian Reflectance Priors (2022, unknown): “Then, following works [3], [31], [32] that avoid potential color artifacts and reduce computational complexity, the color-corrected underwater image U is converted from RGB color space to HSV color space that is more robust to illumination variations.”
• Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model (2018, methodology): “From left to right: observed images, results by a specialized method [43] and the proposed method with model (6).”
• An Underwater Image Enhancement Benchmark Dataset and Beyond (2019, methodology): “From left to right are raw underwater images, and the results of Fusion-based [29], Retinex-based [31], Two-step-based [30], UDCP [35], Red Channel [40], Histogram Prior [42], Regression-based [37], Blurriness-based [43], GDCP [38], and reference images.”
• Underwater image enhancement via extended multi-scale Retinex (2017, background): “Fu et al. [18] also presented a Retinex based enhancement approach for the single underwater image.”
• Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network (2020, methodology): “A retinex-based (RB) approach [5] is proposed to enhance a single underwater image, and there are three major steps: a simple and effective color correc-”
• U-Shape Transformer for Underwater Image Enhancement (2021, methodology): “Retinex based [11] method could improve the image contrast to a certain extent, but the color of the enhanced image is unnatural.”