# Enhancement of Underwater Images with Retinex Transmission Map and Adaptive Color Correction  
[PERSON]  
1 School of Ocean Information Engineering, Jimel University, Xiamen 361021, China 11 School of Informatics, Xiamen University, Xiamen 361005, China 11 School of Computing Engineering, Jimel University, Xiamen 361021, China 11 School of Ocean Information Engineering, Jimel University, Xiamen 361021, China 11 School of Informatics, Xiamen University, Xiamen 361005, China 11 School of Computing Engineering, Jimel University, Xiamen 361021, China 11 School of Information Engineering, Jimel University, Xiamen 361021, China 11 School of Computing Engineering, Jimel University, Xiamen 3610021, China 11 School of Computing  
Another line of research is physical model unrelated, aiming to modify image pixel values to improve visual quality. One example is the Fusion [7] method which blends a contrast-enhanced image and a color-corrected image in a multi-scale fusion strategy. Fusion has achieved remarkable results from the point of color and detail recovery. However, it is prone to introduce red artifacts after enhancement. Recently deep-learning based underwater image enhancement methods [3; 8] have shown promising results. This category of methods takes a data-drive approach to train deep neural networks directly, usually regardless of the underwater imaging models. Despite the success, they often demand heavy computation and high memory which limits their usage in resource-limited application scenarios, such as underwater vision for ROVs and AUVs.  
In this work, we propose a hybrid underwater enhancement method consisting of addressing the inverse problem and color correction. Different from DCP variant methods, our method does not rely on channel priors. Moreover, it aims to decouple the estimation of transmission map from the estimation of background light, avoiding the error accumulation problem. To this end, we perform global white balance before estimating the transmission map, and comprehensively use the multi-scale Retinex [9] to obtain a three-channel transmission map. Gaussian filtering is used to obtain the local backscattered light as the background light value. After addressing the inverse problem, a final adaptive color correction strategy is proposed to further improve the enhancement performance in case of possible unsatisfactory white balance results or unfaithful local backscattered light estimation.  
To demonstrate superiority of the proposed method, we conduct quantitative and qualitative comparison experiments of our method and state-of-the-art (SOTA) underwater image enhancement methods on the Underwater Image Enhancement Benchmark (UIEB) [10] dataset. In quantitative comparison experiments, we perform both all-reference evaluation and non-reference evaluation.  
The contributions of this paper are summarized as follows:  
* A transmission map estimation method based on white balance and multi-scale Retinex is proposed, which does not need unknown backscattered light and avoids an error accumulation problem and block effect. The calculation is relatively simple, and no optimization by guided filtering [11] is required;
* In order to deal with possible unsatisfactory preliminary recovery results in some cases, an adaptive color correction method is proposed, which cleverly choose between two color correction procedures and prevent channel stretching imbalance;
* We conduct comprehensive quantitative and qualitative experiments on our method and compare them with SOTA underwater image enhancement methods. Experimental results demonstrate the superiority of the proposed method which achieves the best performance in terms of full-reference image quality assessment. In addition, it also achieves superior performance in the non-reference evaluation.