Book chapter
A Rapid Scene Depth Estimation Model Based on Underwater Light Attenuation Prior for Underwater Image Restoration
Advances in Multimedia Information Processing – PCM 2018, pp.678-688
Lecture Notes in Computer Science, Springer International Publishing
09/2018
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Abstract
Underwater images present blur and color cast, caused by light absorption and scattering in water medium. To restore underwater images through image formation model (IFM), the scene depth map is very important for the estimation of the transmission map and background light intensity. In this paper, we propose a rapid and effective scene depth estimation model based on underwater light attenuation prior (ULAP) for underwater images and train the model coefficients with learning-based supervised linear regression. With the correct depth map, the background light (BL) and transmission maps (TMs) for R-G-B light are easily estimated to recover the true scene radiance under the water. In order to evaluate the superiority of underwater image restoration using our estimated depth map, three assessment metrics demonstrate that our proposed method can enhance perceptual effect with less running time, compared to four state-of-the-art image restoration methods.
Details
- Title
- A Rapid Scene Depth Estimation Model Based on Underwater Light Attenuation Prior for Underwater Image Restoration
- Creators
- Wei Song - College of Information Technology, Shanghai Ocean University, Shanghai, ChinaYan Wang - College of Information Technology, Shanghai Ocean University, Shanghai, ChinaDongmei Huang - College of Information Technology, Shanghai Ocean University, Shanghai, ChinaDian Tjondronegoro - Southern Cross University, Gold Coast, Australia
- Publication Details
- Advances in Multimedia Information Processing – PCM 2018, pp.678-688
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing; Cham
- Identifiers
- 991012926976402368
- Copyright
- (c) Springer Nature Switzerland AG 2018.
- Academic Unit
- Faculty of Science and Engineering; School of Business and Tourism
- Language
- English
- Resource Type
- Book chapter