Single Underwater Image Restoration Based on Adaptive Transmission Fusion

Abstract

Underwater images are often deteriorated with blurring, darkness, poor visual quality of low contrast, and color diminishing. This is mainly due to the fact that the light is exponentially attenuated while traveling through water and the strength of attenuation is color dependent. After constructing a simplified image formation model, this paper proposes a new strategy for single underwater image restoration. In light of different perspectives, two distinct transmission coefficient estimation approaches have been developed. One is based on the optical characteristics while the other relies on the essence of image processing knowledge. Subsequently, these two transmission maps are fused to produce the final outcome, which is adaptively weighted by their respective saliency maps. The obtained signal radiance is dissolved through point spread function deconvolution and color compensation to produce the final scene radiance. A variety of underwater images with various scenarios were exploited to evaluate the restoration performance. Experimental results demonstrated the superiority of the proposed algorithm over other competitive methods for underwater image restoration. INDEX TERMS Image restoration, transmission fusion, saliency map, dark channel prior, blurriness detection.

A Non-Reference Evaluation of Underwater Image Enhancement Methods Using a New Underwater Image Dataset

“…Chang [24] developed two distinct transmission coefficient estimation approaches, namely 1) optical characteristics; 2) the essence of image processing knowledge. Weighted by saliency maps, the two transmission maps fused into one transmission map to get the outcome.…”

“…Weighted by saliency maps, the two transmission maps fused into one transmission map to get the outcome. Although the proposed model in [24] is capable of applying distinct enhancements to the background and foreground of an image, its performance compared to other baseline image restoration models is yet to be determined. Song et al [25] developed a conventional model-based method using a manually annotated background lights database.…”

Multi-Purpose Oriented Real-World Underwater Image Enhancement

“…In [10], Yang et al combine maximum scene depth estimation and adaptive color correction to accurately estimate the background light. To taking advantage of both the image-based and model-based approaches, Chang et al [26] compute two transmission maps from distinct perspectives and fuse them weighted by their saliency maps. Also, two different approaches are used to integrate the background light into a more accurate estimation.…”

Underwater Image Restoration Based on Adaptive Color Compensation and Dual Transmission Estimation

“…The visibility restoration of an underwater image improves the visual distance in a scene, which is conducive to underwater rescue and surveys. Studies on underwater image visibility restoration conducted in the past can be categorized into three types, namely (a) improved dark channel prior methods [1][2][3][4][5][6][7][8][9], (b) fusion-based methods [10][11][12][13][14], and (c) deep learning based methods [15][16][17][18]. The underwater image visibility restoration method based on deep learning requires synthetic datasets, which do not contain realistic (practical) information, and usually lead to model over-fitting.…”

“…To summarize, in order to recover a clean image (with clear scene visibility) from a degraded underwater image (i.e., image captured underwater by a camera), different methods can be used [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], amongst which the improved dark channel method based on physical model is often used. There are many types of underwater image restoration methods based on improved dark channel prior, and the most commonly used method is the red-channel underwater restoration method.…”