We propose an underwater image enhancement model inspired by the morphology and function of the teleost fish retina. We aim to solve the problems of underwater image degradation raised by the blurring and nonuniform color-biasing. In particular, the feedback from color-sensitive horizontal cells to cones and a red channel compensation are used to correct the nonuniform color bias. The center-surround opponent mechanism of the bipolar cells and the feedback from amacrine cells to interplexiform cells then to horizontal cells serve to enhance the edges and contrasts of the output image. The ganglion cells with color-opponent mechanism are used for color enhancement and color correction. Finally, we adopt a luminance based fusion strategy to reconstruct the enhanced image from the outputs of ON and OFF pathways of fish retina. Our model utilizes the global statistics (i.e., image contrast) to automatically guide the design of each low-level filter, which realizes the self adaption of the main parameters. Extensive qualitative and quantitative evaluations on various underwater scenes validate the competitive performance of our technique. Our model also significantly improves the accuracy of transmission map estimation and local feature point matching using the underwater image. Our method is a single image approach that does not require the specialized prior about the underwater condition or scene structure.

Benefitting from insensitivity to light and high penetration of foggy environments, infrared cameras are widely used for sensing in nighttime traffic scenes. However, the low contrast and lack of chromaticity of thermal infrared (TIR) images hinder the human interpretation and portability of high-level computer vision algorithms. Colorization to translate a nighttime TIR image into a daytime color (NTIR2DC) image may be a promising way to facilitate nighttime scene perception. Despite recent impressive advances in image translation, semantic encoding entanglement and geometric distortion in the NTIR2DC task remain under-addressed. Hence, we propose a toP-down attEntion And gRadient aLignment based generative adversarial network, referred to as PearlGAN. A top-down guided attention module and an elaborate attentional loss are first designed to reduce the semantic encoding ambiguity during translation. Then, a structured gradient alignment loss is introduced to encourage edge consistency between the translated and input images. In addition, pixel-level annotation is carried out on a subset of FLIR and KAIST datasets to evaluate the semantic preservation performance of multiple translation methods. Furthermore, a new metric is devised to evaluate the geometric consistency in the translation process. Extensive experiments demonstrate the superiority of the proposed PearlGAN over other image translation methods for the NTIR2DC task. The source code and labeled segmentation masks will be available at https://github.com/FuyaLuo/PearlGAN/ .

The morphology of retinal vessels is closely associated with many kinds of ophthalmic diseases. Although huge progress in retinal vessel segmentation has been achieved with the advancement of deep learning, some challenging issues remain. For example, vessels can be disturbed or covered by other components presented in the retina (such as optic disc or lesions). Moreover, some thin vessels are also easily missed by current methods. In addition, existing fundus image datasets are generally tiny, due to the difficulty of vessel labeling. In this work, a new network called SkelCon is proposed to deal with these problems by introducing skeletal prior and contrastive loss. A skeleton fitting module is developed to preserve the morphology of the vessels and improve the completeness and continuity of thin vessels. A contrastive loss is employed to enhance the discrimination between vessels and background. In addition, a new data augmentation method is proposed to enrich the training samples and improve the robustness of the proposed model. Extensive validations were performed on several popular datasets (DRIVE, STARE, CHASE, and HRF), recently developed datasets (UoA-DR, IOSTAR, and RC-SLO), and some challenging clinical images (from RFMiD and JSIEC39 datasets). In addition, some specially designed metrics for vessel segmentation, including connectivity, overlapping area, consistency of vessel length, revised sensitivity, specificity, and accuracy were used for quantitative evaluation. The experimental results show that, the proposed model achieves state-of-the-art performance and significantly outperforms compared methods when extracting thin vessels in the regions of lesions or optic disc. Source code is available at https://www.github.com/tyb311/SkelCon .

Recently, a new density-peak-based clustering method, called clustering with local density peaks-based minimum spanning tree (LDP-MST), was proposed, which has several attractive merits, e.g., being able to detect arbitrarily shaped clusters and not very sensitive to noise and parameters. Nevertheless, we also found the limitation of LDP-MST in efficiency. Specifically, LDP-MST has O(NlogN+M²) time, where N denotes the dataset size and M is an intermediate variable denoting the number of local density peaks. As our experimental results reveal, when processing large-size datasets, the value of M could be very large and consequently those steps of LDP-MST involving O(M²) time term would be time-consuming. And in the worst case, the value of M could be very close to that of N, which means that the time complexity of LDP-MST could be O(N²) in the worst case of M. In this study, we use more efficient algorithms to implement those steps of LDP-MST that involve the O(M²) time term such that the proposed method, Fast LDP-MST, has O(NlogN) time complexity even if MN. Our experiments demonstrate that Fast LDP-MST is overall more efficient than LDP-MST on large-size datasets, without sacrificing the merits of LDP-MST in effectiveness, robustness, and user-friendliness.

Object detection is an important task for self-driving vehicles or advanced driver assistant systems (ADASs). Additionally, visual selective attention is a crucial neural mechanism in a driver’s vision system that can rapidly filter out unnecessary visual information in a driving scene. Some existing models detect all objects in driving scenes from the aspect of computer vision. However, in a rapidly changing driving environment, detecting salient or critical objects appearing in drivers’ interested or safety-relevant areas is more useful for ADASs. In this paper, we managed to detect salient and critical objects based on drivers’ fixation regions. To this end, we built an augmented eye tracking object detection (ETOD) dataset based on driving videos with multiple drivers’ eye movement collected by Deng et al. Furthermore, we proposed a real-time salient object detection network named increase-decrease YOLO (ID-YOLO) to discriminate the critical objects within the drivers’ fixation region. The proposed ID-YOLO shows excellent detection of major objects that drivers are concerned about during driving. Compared with the present object detection models in autonomous and assisted driving systems, our object detection framework simulates the selective attention mechanism of drivers. Thus, it does not detect all of the objects appearing in the driving scenes but only detects the most relevant ones for driving safety. It can largely reduce the interference of irrelevant scene information, showing potential practical applications in intelligent or assisted driving systems.