Underwater image restoration via backscatter pixel prior and color compensation

Underwater Image Restoration via Information Distribution and Light Scattering Prior

Underwater images captured by optical cameras can be degraded by light attenuation and scattering, which leads to deteriorated visual image quality. The technique of underwater image enhancement plays an important role in a wide range of subsequent applications such as image segmentation and object detection. To address this issue, we propose an underwater image enhancement framework which consists of an adaptive color restoration module and a haze-line based dehazing module. First, we employ an adaptive color restoration method to compensate the deteriorated color channels and restore the colors. The color restoration module consists of three steps: background light estimation, color recognition, and color compensation. The background light estimation determines the image is blueish or greenish, and the compensation is applied in red-green or red-blue channels. Second, the haze-line technique is employed to remove the haze and enhance the image details. Experimental results show that the proposed method can restore the color and remove the haze at the same time, and it also outperforms several state-of-the-art methods on three publicly available datasets. Moreover, experiments on an underwater object detection dataset show that the proposed underwater image enhancement method is able to improve the accuracy of the subsequent underwater object detection framework.

Underwater image enhancement using adaptive color restoration and dehazing.

The absorption and scattering caused by the underwater medium degrade the quality of underwater optical imaging, which limits the further development of underwater tasks. Recently, transformer-based methods have shown the same excellent performance as convolutional neural networks (CNNs) in various vision tasks, but the huge parameters of such networks hinder their application deployment. In this article, we propose novel adaptive group attention (AGA), which can dynamically select visually complementary channels based on the dependencies, reducing the number of further attention parameters. The AGA is applied in the Swin Transformer module and used to design an end-to-end underwater image enhancement network. The network also introduces the multiscale cascade module and the channel attention mechanism. This article conducted ablation study and qualitative and quantitative comparisons on public datasets, and the results show that the application of AGA significantly compresses the model size while ensuring performance, and other application components have the significant gain on the network. Compared with other advanced methods, the network in this article has outstanding performance.

Underwater Image Enhancement via Adaptive Group Attention-Based Multiscale Cascade Transformer

Cross-view enhancement network for underwater images

For the enhancement process of underwater images taken in various water types, previous methods employ the simple image formation model, thus obtaining poor restoration results. Recently, a revised underwater image formation model (i.e., the Akkaynak-Treibitz model) has shown better robustness in underwater image restoration, but has drawn little attention due to its complexity. Herein, we develop a dehazing method utilizing the revised model, which depends on the scene depth map and a color correction method to eliminate color distortion. Specifically, we first design an underwater image depth estimation method to create the depth map. Subsequently, according to the depth value of each pixel, the backscatter is estimated and removed by the channel based on the revised model. Furthermore, we propose a color correction approach to adjust the global color distribution of the image automatically. Our method only uses a single underwater image as input to eliminate lightwave absorption and scattering influence. Compared with state-of-the-art methods, both subjective and objective experimental results show that our approach can be applied to various real-world underwater scenes and has better contrast and color.

Dan Zhang

Papers

Underwater image restoration via depth map and illumination estimation based on a single image