A Revised Underwater Image Formation Model

By Derya Akkaynak, Tali Treibitz. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018

430 citations recorded by the source corpus.

The current underwater image formation model descends from atmospheric dehazing equations where attenuation is a weak function of wavelength. We recently showed that this model introduces significant errors and dependencies in the estimation of the direct transmission signal because underwater, light attenuates in a wavelength-dependent manner. Here, we show that the backscattered signal derived from the current model also suffers from dependencies that were previously unaccounted for. In doing so, we use oceanographic measurements to derive the physically valid space of backscatter, and further show that the wideband coefficients that govern backscatter are different than those that govern direct transmission, even though the current model treats them to be the same. We propose a revised equation for underwater image formation that takes these differences into account, and validate it through in situ experiments underwater. This revised model might explain frequent instabilities of current underwater color reconstruction models, and calls for the development of new methods.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/88410da1636ed7ae840af5feb1dffad99286c0b4

How researchers see this paper

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

In Biomedical Engineering

1. Chunle Guo
2. Chongyi Li
3. Wenqi Ren
4. Runmin Cong
5. Junhui Hou
6. Dacheng Tao
7. Sam Kwong
8. Saeed Anwar

In Computer Vision and Pattern Recognition

1. Chongyi Li
2. Chunle Guo
3. Wenqi Ren
4. Runmin Cong
5. Junaed Sattar
6. Saeed Anwar
7. M. Islam
8. Dacheng Tao

In Media Technology

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

Citations in the corpus

• Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset (2018, background): “Recently, it was shown that the commonly used underwater image formation model [5] is inaccurate and introduces reconstruction errors [32].”
• Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception (2020, background): “[2, 3] introduces a revised image formation model that accounts for the unique characteristics of underwater light propagation; this contributes to a more accurate estimation of range-dependent attenuation and backscatter [57].”
• Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding (2021, methodology): “A new underwater image formation model was proposed in [19].”
• An Underwater Image Enhancement Benchmark Dataset and Beyond (2019, methodology): “image color correction method based on a revised underwater image formation model [6] which is physically accurate.”
• Fast Underwater Image Enhancement for Improved Visual Perception (2019, background): “light propagation and image formation characteristics [10], [12], [13] can be incorporated into the optimization for more accurate image restoration.”