Underwater image enhancement via extended multi-scale Retinex

By Shu Zhang, Ting Wang, Junyu Dong, Hui Yu. Neurocomputing, 2017

404 citations recorded by the source corpus.

Underwater exploration has become an active research area over the past few decades. The image enhancement is one of the challenges for those computer vision based underwater researches because of the degradation of the images in the underwater environment. The scattering and absorption are the main causes in the underwater environment to make the images decrease their visibility, for example, blurry, low contrast, and reducing visual ranges. To tackle aforementioned problems, this paper presents a novel method for underwater image enhancement inspired by the Retinex framework, which simulates the human visual system. The term Retinex is created by the combinations of Retina and Cortex. The proposed method, namely LAB-MSR, is achieved by modifying the original Retinex algorithm. It utilizes the combination of the bilateral filter and trilateral filter on the three channels of the image in CIELAB color space according to the characteristics of each channel. With real world data, experiments are carried out to demonstrate both the degradation characteristics of the underwater images in different turbidities, and the competitive performance of the proposed method.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/8e319b00d34de2a977dc73d05132abb4ad15db06

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. Runmin Cong
4. Wenqi Ren
5. Chunli Zhu
6. Liheng Bian
7. Lintao Peng
8. Peixian Zhuang

In Media Technology

1. Chongyi Li
2. Runmin Cong
3. Junhui Hou
4. Chunle Guo
5. Wenqi Ren
6. Junaed Sattar
7. M. Islam
8. Chunli Zhu

Citations in the corpus

• Sea-Thru: A Method for Removing Water From Underwater Images (2019, background): “, [30, 62]), and a recent work showed that there is a direct linear relationship between atmospheric image dehazing and Retinex [31].”
• Underwater Image Enhancement With Hyper-Laplacian Reflectance Priors (2022, positive): “Zhang et al. [16] extended a multi-scale retinex method for underwater image enhancement.”
• Fast Underwater Image Enhancement for Improved Visual Perception (2019, background): “ation [10], scattering, and other optical properties of the waterbodies cause irregular non-linear distortions [5], [11] which”
• Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception (2020, methodology): “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.”
• An Underwater Image Enhancement Benchmark Dataset and Beyond (2019, background): “[32] proposed an extended multiscale Retinex based underwater image enhancement method.”
• Using labeled data to evaluate change detectors in a multivariate streaming environment (2009, positive): “PIDNet [20] proposes a real-time semantic segmentation network inspired by PID Controller.”
• U-Shape Transformer for Underwater Image Enhancement (2021, methodology): “[35] proposed a multi-scale UIE method based on the retinex model.”