Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset

By Dana Berman, Deborah Levy, Shai Avidan, Tali Treibitz. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018

667 citations recorded by the source corpus.

Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since the water type is unknown, we evaluate different parameters out of an existing library of water types. Each type leads to a different restored image and the best result is automatically chosen based on color distribution. We also contribute a dataset of 57 images taken in different locations. To obtain ground truth, we placed multiple color charts in the scenes and calculated its 3D structure using stereo imaging. This dataset enables a rigorous quantitative evaluation of restoration algorithms on natural images for the first time.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c7de57d0b3e01cece3b50e9d87595f9e26596552

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

In Media Technology

1. Chunli Zhu
2. Liheng Bian
3. Lintao Peng
4. Runmin Cong
5. Chongyi Li
6. Peixian Zhuang
7. Junhui Hou
8. Chunle Guo

Citations in the corpus

• Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map (2019, background): “More recently, some researchers have began to work on underwater images databases [39], [40].”
• Underwater Image Enhancement via Weighted Wavelet Visual Perception Fusion (2024, background): “Berman et al. [29] considered multiple spectral profiles of different water types and estimated two additional global parameters to defog underwater images, which could correct color distortion and restore the 3D structure of underwater scenes, but it had relatively high time complexity.”
• U-Shape Transformer for Underwater Image Enhancement (2021, methodology): “Those two metrics are of valuable reference, but cannot as absolute justifications [26] [3], for they are non-sensitive to color artifacts & casts and biased to some features.”
• An Underwater Image Enhancement Benchmark Dataset and Beyond (2019, methodology): “dataset providing raw images, TIF files, camera calibration files, and distance maps5 [62].”
• Fast Underwater Image Enhancement for Improved Visual Perception (2019, methodology): “, haze-lines, dark channel prior [13], etc.”
• Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding (2021, methodology): “We refer the readers to [18] and [53] for the discussions and visual examples.”
• Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception (2020, background): “Some of these aspects can be modeled and estimated by physics-based solutions, particularly for dehazing [7], color correction [10], water removal [3], etc.”