This letter introduces an underwater image enhancement method to handle low contrast and color cast of underwater images. Firstly, with the help of hierarchical searching technique, we propose a novel backscattered light estimation method. And in this procedure, a novel scoring formula is considered into our method, which comprehensively considers multiple prior knowledge. Then, we generalize underwater dark channel prior (UDCP) approach to obtain more robust transmission estimation. In addition, we also develop a white balance method to further modify the appearance of the resultant image. Extensive experiments on real-world images demonstrate that the proposed method outperforms several previous image restoration or enhancement works.

Due to the absorption and scattering of light, underwater captured images often face serious quality degradation issues. In this letter, we propose to cope with the aforementioned issues via piecewise color correction and dual prior optimized contrast enhancement. Specifically, we first present the piecewise color correction method using the maximum mean and two gain factors to correct the color cast of each color channel. Then, we propose a dual prior optimized contrast enhancement method, which relies on the spatial and texture priors to decompose the base layer and detail layer of the V channel in HSV color space. Meanwhile, we employ different enhancement strategies in different layers to enhance the contrast and texture detail of underwater images. Our extensive experiments on several benchmark datasets show that our method outperforms eleven compared state-of-the-art methods. Moreover, our method has good generalization capability for fog and low-light images. The code is available at https://github.com/Li-Chongyi/PCDE .

Images captured under poor lighting conditions often suffer from low brightness, low contrast, color distortion, and noise. The function of low-light image enhancement is to improve the visual effect of such images for subsequent processing. Recently, deep learning has been used more and more widely in image processing with the development of artificial intelligence technology, and we provide a comprehensive review of the field of low-light image enhancement in terms of network structure, training data, and evaluation metrics. In this paper, we systematically introduce low-light image enhancement based on deep learning in four aspects. First, we introduce the related methods of low-light image enhancement based on deep learning. We then describe the low-light image quality evaluation methods, organize the low-light image dataset, and finally compare and analyze the advantages and disadvantages of the related methods and give an outlook on the future development direction.

Due to the complex imaging mechanism, underwater images often suffer from multiple degradation issues, such as color cast, blurry detail, and low contrast, which affect the extraction of valuable information. To deal with these degradation issues, a simple yet effective underwater image quality improvement method based on color, datail and contrast restoration (CDCR) is developed, which consists of three key modules: a well-preserved finding-driven color balance module (CBM), a linear saturation transformation-based discriminant function-based detail restoration module (DRM), and a transmission minimization-oriented contrast restoration module (CRM). First, the CBM explores a well-preserved channel finding and employs a channel compensation strategy to balance the color differences among three color channels. Second, the DRM uses a piecewise underwater image saturation estimation strategy, which takes the various spectral properties of water into account and designs an additional linear saturation transformation-based discriminant function to prevent the transmission from being under-estimated. At last, the CRM estimates a global backscatter light based on transmission minimization and further improves the contrast by locally removing the backscatter light of the base layer. Our restored image is appealing in its natural color, fine details, and high contrast. Extensive experiments on three underwater image enhancement datasets show that our CDCR achieves better results than state-of-the-art methods, i.e., compared with the second-best method, the average PCQI and UIQM values of our method increase by 5.7% and 0.2%, and the average Blur and DFAD values of our method decrease by 8.0% and 5.3%. Meanwhile, experiments further suggest that the rate of new visible edges and the quality of contrast restoration of our CDCR at least increase by 7.7% and 51.2% in most tested sandstorm and foggy images, respectively, which demonstrates that our method has a good generalization capability for sandstorm and foggy image restoration.

Recently, image restoration has attracted considerable attention from researchers, and these methods generally restore degraded images based on the atmospheric scattering model (ATSM) and retinex model (RM). The two models only take into the single attenuation process during imaging, thereby introducing undesirable results. To deal with this issue, we propose an image restoration method based on a generalized image formation model (GIFM). First, unlike the existing image restoration methods, we rebuild a novel image formation model, which describes the light attenuation process that includes the light source-scene path and scene-sensor path. Second, we construct an objective optimization function to decompose a degraded image into a color distorted component and color corrected component, and an augmented Lagrange multiplier-based alternating direction minimization algorithm is provided to solve the optimization problem. At last, we fully consider the advantages of the small-scale neighborhood and large-scale neighborhood in image restoration, and an image itself brightness-based weighted fusion strategy is proposed to balance brightness enhancement and contrast improvement. Extensive experiments on three image enhancement datasets show that our GIFM achieves better results than state-of-the-art methods. Experiments further suggest that our GIFM performs well for image restoration of extreme scenes, keypoint detection, object detection and image segmentation.

The driver gaze zone is an indicator of a driver’s attention and plays an important role in the driver’s activity monitoring. Due to the bad initialization of point-cloud transformation, gaze zone systems using RGB-D cameras and ICP (Iterative Closet Points) algorithm do not work well under long-time head motion. In this work, a solution for a continuous driver gaze zone estimation system in real-world driving situations is proposed, combining multi-zone ICP-based head pose tracking and appearance-based gaze estimation. To initiate and update the coarse transformation of ICP, a particle filter with auxiliary sampling is employed for head state tracking, which accelerates the iterative convergence of ICP. Multiple templates for different gaze zone are applied to balance the templates revision of ICP under large head movement. For the RGB information, an appearance-based gaze estimation method with two-stage neighbor selection is utilized, which treats the gaze prediction as the combination of neighbor query (in head pose and eye image feature space) and linear regression (between eye image feature space and gaze angle space). The experimental results show that the proposed method outperforms the baseline methods on gaze estimation, and can provide a stable head pose tracking for driver behavior analysis in real-world driving scenarios.