Diving Deeper into Underwater Image Enhancement: A Survey <s> Benchmark Results <s> Images taken underwater often suffer color distortion and low contrast because of light scattering and absorption. An underwater image can be modeled as a blend of a clear image and a background light, with the relative amounts of each determined by the depth from the camera. In this paper, we propose two neural network structures to estimate background light and scene depth, to restore underwater images. Experimental results on synthetic and real underwater images demonstrate the effectiveness of the proposed method. <s> BIB001 </s> Diving Deeper into Underwater Image Enhancement: A Survey <s> Benchmark Results <s> In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts. To address this problem, we propose a convolutional neural network based image enhancement model, i.e., UWCNN, which is trained efficiently using a synthetic underwater image database. Unlike the existing works that require the parameters of underwater imaging model estimation or impose inflexible frameworks applicable only for specific scenes, our model directly reconstructs the clear latent underwater image by leveraging on an automatic end-to-end and data-driven training mechanism. Compliant with underwater imaging models and optical properties of underwater scenes, we first synthesize ten different marine image databases. Then, we separately train multiple UWCNN models for each underwater image formation type. Experimental results on real-world and synthetic underwater images demonstrate that the presented method generalizes well on different underwater scenes and outperforms the existing methods both qualitatively and quantitatively. Besides, we conduct an ablation study to demonstrate the effect of each component in our network. <s> BIB002
The benchmark results for each technique 8 on UIEBD  dataset are reported in Table 2 . The quantitative exper- BIB001 The results are reported for the methods having the source code or executables available or the respected authors agreed to provide the results on the dataset. iments are conducted on UIEBD  because it is, to the best of our knowledge, the only one dataset which provides the corresponding reference images for image quality assessment. The results by using reference images can provide realistic feedback on the quality of enhanced results to some extent. Moreover, in case of multiple variants of the same algorithm, all the results are reported. We encourage the readers to consult the original paper for a detailed analysis of each variant of the same model. The results are presented via the metrics mentioned earlier. It is to be noted here that the PSNR, SSIM, PCQI, UCIQE, and UIQM, the higher, the better while the MSE, the lower, the better. Also, to be fair amidst all the methods under consideration, we resize the output of the network where the predicted image is a scaleddown version of the underwater scene input. From Table 2, DUIENet  results are the best among the competitors while the UWCNN BIB002 performs worst due to training on the synthesized underwater images which are different from the images in the UIEBD  . However, it is challenging to state the superiority of one method against the others due to many factors involved, for example, the number of parameters, the depth of network, training images, patch size, number of channels and loss function, etc. To compare fairly, most of these determinants should be kept consistent. To further validate the performance of different deep algorithms, we conduct qualitative comparisons on diverse underwater images from different datasets in the next section. 