Distorted underwater image reconstruction for an autonomous underwater vehicle based on a self-attention generative adversarial network

Joint polarization detection and degradation mechanisms for underwater image enhancement

Light absorption and scattering exist in the underwater environment, which can lead to blurring, reduced brightness, and color distortion in underwater images. Polarized images have the advantages of eliminating underwater scattering interference, enhancing contrast, and detecting material information of the object in underwater detection. In this paper, from the perspective of polarization imaging, different concentrations (0.15 g/ml, 0.30 g/ml, and 0.50 g/ml), different wave bands (red, green, and blue), different materials (copper, wood, high-density PVC, aluminum, cloth, foam, cloth sheet, low-density PVC, rubber, and porcelain tile), and different depths (10 cm, 20 cm, 30 cm, and 40 cm) are set up in a chamber for the experimental environment. By combining the degradation mechanism of underwater images and the analysis of polarization detection results, it is proved that the degree of polarization images have greater advantages than degree of linear polarization images, degree of circular polarization images, S1, S2, and S3 images, and visible images underwater. Finally, a fusion algorithm of underwater visible images and polarization images based on compressed sensing is proposed to enhance underwater degraded images. To improve the quality of fused images, we introduce orthogonal matching pursuit (OMP) in the high-frequency part to improve image sparsity and consistency detection in the low-frequency part to improve the image mutation phenomenon. The fusion results show that the peak SNR values of the fusion result maps using OMP in this paper are improved by 32.19% and 22.14% on average over those using backpropagation and subspace pursuit methods. With different materials and concentrations, the underwater image enhancement algorithm proposed in this paper improves information entropy, average gradient, and standard deviation by 7.76%, 18.12%, and 40.8%, respectively, on average over previous algorithms. The image NIQE value shows that the image quality obtained by this paper's algorithm is improved by about 69.26% over the original S0 image.

Inverted pyramid frame forward and backward prediction for distorted video by water waves

There has been much research on how to restore a single image from distorted video. Random water surface variation, an inability to model the surface, and multiple factors in the imaging processing leading to different geometric distortions in each frame are among the challenges. This paper proposes an inverted pyramid structure based on the cross optical flow registration approach and a multi-scale weight fusion method based on wavelet decomposition. The inverted pyramid based on the registration method is used to estimate the original pixel positions. A multi-scale image fusion method is applied to fuse the two inputs processed by optical flow and backward mapping, and two iterations are proposed to improve the accuracy and stability of the output video. The method is tested on several reference distorted videos and our videos, which were obtained through our experimental equipment. The obtained results exhibit significant improvements over other reference methods. The corrected videos obtained with our approach have a higher degree of sharpness, and the time required to restore the videos is significantly reduced.

Dual-path joint correction network for underwater image enhancement

Acquired underwater images often suffer from severe quality degradation, such as color shift and detail loss due to suspended particles' light absorption and scattering. In this paper, we propose a Dual-path Joint Correction Network (DJC-NET) to cope with the above degenerate issues, preserving different unique properties of underwater images in a dual-branch way. The design of the light absorption correction branch is to improve the selective absorption of light in water and remove color distortion, while the light scattering correction branch aims to improve the blur caused by scattering. Concretely, in the light absorption correction path, we design the triplet color feature extraction module, which balances the triplet color distribution of the degraded image through independent feature learning between R, G, and B channels. In the light scattering correction path, we develop a dual dimensional attention mechanism to extract the texture information from the features, aiming to recover sufficient details by more effective feature extraction. Furthermore, our method utilizes the multi-scale U-net to adaptively fusion features from different paths to generate enhanced images. Extensive visual and objective experimental results demonstrate that our method outperforms state-of-the-art methods in various underwater scenes.

Underwater image restoration via background light estimation and depth map optimization

In underwater images, the significant sources of distortion are light attenuation and scattering. Existing underwater image restoration technologies cannot deal with the poor contrast and color distortion bias of underwater images. This work provides a new underwater image restoration approach relying on depth map optimization and background light (BL) estimation. First, we build a robust BL estimation model that relies on the prior features of blurriness, smoothness, and the difference between the intensity of the red and blue-green channels. Second, the red-light intensity, difference between light and dark channels, and disparity of red and green-blue channels by considering the hue are used to calculate the depth map. Then, the effect of artificial light sources on the underwater image is removed using the adjusted reversed saturation map. Both the subjective and objective experimental results reveal that the images produced by the proposed technology provide more remarkable visibility and superior color fidelity.

Multi-prior underwater image restoration method via adaptive transmission

Captured underwater images usually suffer from severe color cast and low contrast due to wavelength-dependent light absorption and scattering. These degradation issues affect the accuracy of target detection and visual understanding. The underwater image formation model is widely used to improve the visual quality of underwater images. Accurate transmission map and background light estimation are the keys to obtaining clear images. We develop a multi-priors underwater image restoration method with adaptive transmission (MUAT). Concretely, we first propose a calculation method of the dominant channel transmission to cope with pixel interference, which combines two priors of the difference between atmospheric light and pixel values and the difference between the red channel and the blue-green channel. Besides, the attenuation ratio between the superior and inferior channels is adaptively calculated with the background light to solve the color distortion and detail blur caused by the imaging distance. Ultimately, the global white balance method is introduced to solve the color distortion. Experiments on several underwater scene images show that our method obtains accurate transmission and yields better visual results than state-of-the-art methods.