Emerging From Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer

Abstract

Abstract-Underwater vision suffers from severe effects due to selective attenuation and scattering when light propagates through water. Such degradation not only affects the quality of underwater images but limits the ability of vision tasks. Different from existing methods which either ignore the wavelength dependency of the attenuation or assume a specific spectral profile, we tackle color distortion problem of underwater image from a new view. In this letter, we propose a weakly supervised color transfer method to correct color distortion, which relaxes the need of paired underwater images for training and allows for the underwater images unknown where were taken. Inspired by Cycle-Consistent Adversarial Networks, we design a multiterm loss function including adversarial loss, cycle consistency loss, and SSIM (Structural Similarity Index Measure) loss, which allows the content and structure of the corrected result the same as the input, but the color as if the image was taken without the water. Experiments on underwater images captured under diverse scenes show that our method produces visually pleasing results, even outperforms the art-of-the-state methods. Besides, our method can improve the performance of vision tasks.

An Underwater Image Enhancement Benchmark Dataset and Beyond

“…Subjective comparisons on underwater images from testing set. From left to right are raw underwater images, and the results of fusion-based [31], retinex-based [33], histogram prior [45], blurriness-based [46], GDCP [40], Water CycleGAN [53], Dense GAN [55], the proposed Water-Net, and reference images. , retinex-based [33], histogram prior [45], blurriness-based [46], GDCP [40], Water CycleGAN [53], Dense GAN [55], and the proposed Water-Net.…”

“…Besides, we also provide the standard deviation of the results by each method on challenging set. We exclude the scores of Water CycleGAN [53] and Dense GAN [55] due to their obviously unpleasing results as shown in Fig. 14. Our Water-Net receives the highest average score and lowest standard deviation, which indicates our method produces better results from a subjective perspective and has more robust performance.…”

“…With the candidate underwater images, the potential reference images are generated by 12 image enhancement methods, including 9 underwater image enhancement methods (i.e., fusion-based [31], two-step-based [32], retinex-based [33], UDCP [37], regression-based [39], GDCP [40], Red Channel [42], histogram prior [45], and blurriness-based [46]), 2 image dehazing methods (i.e., DCP [35] and MSCNN [66]), and 1 commercial application for enhancing underwater images (i.e., dive+ 8 ). We exclude the recent deep learning-based methods due to their limited generalization capability to the diverse real-world underwater images and the fixed size of network output [53], [55]. The source codes of all the employed methods except the fusion-based [31] are provided by their authors.…”

Underwater Color Restoration Using U-Net Denoising Autoencoder

“…The color restoration might be limited to certain types of color degradation appearance. [13] As an improvement over the aforementioned data generation method, Li et al, 2018 [14] and Fabbri et al, 2018 [15] used CycleGAN [16] for generating underwater images. After synthesizing the data, it was later used for training their color restoration model.…”

“…The previously mentioned deep learning methods showed good performance in restoring the color. However in certain scenarios, they led to an unrealistic color correction of underwater images as in Li et al, 2018 [14]. The training dataset lacked true colors of underwater structures such as coral reefs and fish.…”