Underwater Image Enhancement Based on a Spiral Generative Adversarial Framework

Abstract

Underwater image enhancement has drawn much attention due to the significance of underwater vision. Although considerable progress has been made in this field, a key problem remains unsolved: how can we extract and enhance minutiae while trying to remove the noise caused by scattering and attenuation? To address this limitation, we propose a new underwater image enhancement technique with a novel spiral generative adversarial framework, named Spiral-GAN, which can effectively recover real-world underwater images with more details, vivid colors and better contrast. For steady training and color correction, we include the pixel-wise losses that consist of a mean squared error and an angle error in our objective function. In addition, we design our generator with several deconv-conv blocks to preserve the details from the original distorted images. Furthermore, we present a spiral learning strategy for generalizing the enhancing model to effectively recover the real-world underwater images. Finally, we perform a number of qualitative and quantitative evaluations that suggest that our proposed approach can efficiently enhance the quality of underwater images, which can be further used for underwater object detection. INDEX TERMS Underwater image enhancement, Generative adversarial networks, Spiral training.

Fast underwater image enhancement based on a generative adversarial framework

“…The training datasets for WaterNet (Li et al, 2019) contain a variety of different underwater scenes, effectively improving the adaptability of the network. Han et al (Han et al, 2020) also formulated the underwater image enhancement task as an application in image translation and proposed Spiral-GAN, which is a novel spiral generative adversarial framework to alleviate the problem of poor generalization performance. To solve the problems of color casts and low contrast caused by wavelength and distance, Li et al (Li et al, 2021) proposed an underwater image enhancement network that integrates medium transmissionguided and multicolor space.…”

“…In this paper, we develop a fast and efficient model called FSpiral-GAN that can greatly accelerate the processing speed for large-size images and maintain a high quality of the enhanced images. Our model, which is based on a generative adversarial framework, has one generator and N discriminators following the spiral strategy of Spiral-GAN (Han et al, 2020). To improve the efficiency of the model and maintain good quality in the generated images, we design a lightweight generator structure by using the encoder and decoder structure with equal upsampling blocks (EUBs), equal downsampling blocks (EDBs) and RCABs.…”

“…We follow the training setup of UGAN (Fabbri et al, 2018) and Sprial-GAN (Han et al, 2020) to enhance large-size images. To stabilize the adversarial training process and reduce the impact of overfitting, we take advantage of our previous work, Spiral-GAN (Han et al, 2020), and follow the spiral strategy to train the proposed generator and discriminators. Our model has one generator and N discriminators, where N is the number of spiral loops.…”

“…Some researchers have indicated that the model parameters and processing time could be reduced by using knowledge distillation (Ren et al, 2021). However, for underwater image enhancement tasks, reducing channels and layers is more likely to cause color shifting and distortion (Guo et al, 2019;Han et al, 2020;Zhuang, 2021). Simultaneously improving underwater image quality and translation efficiency is challenging.…”

“…It has been shown that many GAN-based models can improve the perceptual quality of underwater images as well as their contrast and color saturation (Han et al, 2020;Islam et al, 2020). However, GAN-based models can also generate spatially inconsistent styles and introduce artifacts (Li et al, 2019;Wang et al, 2021;Yang et al, 2021b).…”