Hazy images have limited visibility and low contrast. The degradation is expressed by transmission map, which is one of the most important estimates of single image dehazing. Transmission map estimation is an underconstraint problem, and lots of priors have been proposed. Among them, the dark channel prior is widely recognized. However, traditional methods have not fully exploited its power due to improper assumptions or operations, which cause unwanted artifacts. The postrefinement algorithms employed to remove these artifacts in turn undermine the merits of the prior. In this letter, a novel method for estimating transmission map by energy minimization is proposed to solve this problem. The energy function combines the dark channel prior with piecewise smoothness. The method is compared to the state-of-the-art methods and shows outstanding performance.

An $n$ -order marker field is a special binary matrix whose $n\times n$ subregions are all distinct from each other in four orientations. It is commonly used to guide the composing process of position-sensing markers, which can be detected and identified in a camera image with very limited scope or severe visibility problems. Despite the advantages, position-sensing markers are rare and overlooked because generating marker fields is difficult. In this paper, we broaden the definition of marker field, making it more powerful and flexible. Then, we propose bWFC (binary wave function collapse) and its high-speed version, fast-bWFC, to solve the generation problem. The methods are packaged into an open-sourced toolkit named HydraMarker, with which, users not only can generate marker fields on laptops within a short period of time, but also can highly customize them: preset values; fields and subregions in any shape; multifold local uniqueness. Comparative results indicate that the proposed method has superior efficiency, quality, and capability. It makes marker field generation accessible to common marker designers, opening up more possibilities for fiducial markers.

Checkerboard-like markers are widely applied in visual localization applications, including SLAM, augmented reality, robot navigation, and 3-D scene reconstruction. Most corner detectors assume that the marker is attached to a flat surface, which severely limits the placement of the marker and thus reduces the scope of use for these applications. However, in some scenarios, it is not easy to find an ideal flat surface required by most corner detectors in a given scenario, in which case the marker can often only be fixed on a curved surface. Therefore, the accuracy of most corner detectors may be reduced. In this study, a novel method is proposed with subpixel accuracy to detect and locate the corners on a chessboard-like marker, which is either flat or curved. The proposed method fits multisegment curves in a quadratic form to the edges in a checkerboard-like marker. The exact corner positions are considered as the intersections of the corresponding curves with analytical solutions. The proposed method achieves the state-of-the-art performance through experiments, including synthetic corner localization test, real-world stereo vision triangulation experiment, and pose estimation on a curved object, demonstrating the superiority of our method.

Transmission map estimation is one of the most important parts of single image dehazing, which is known as an under-constraint problem. Various assumptions, some have been summarized as priors or models, are proposed to solve this problem. However, many previous methods cannot honestly reflect their theory in the results, due to not considering all of the employed assumptions simultaneously. Meanwhile, most other methods avoiding this defect are with inappropriate assumptions. We try to solve this problem by proposing a method that simultaneously considers the dark channel prior and the piecewise smoothness assumption. It is achieved by minimizing an energy function based on all of the employed assumptions. To maintain a reasonable run time, the minimization problem is mapped into a graph cut problem with a specific graph build strategy. The method is compared with state-of-the-art methods on both synthetic and natural images. Experimental results show that the proposal is promising for haze removal quality.

The quality of oral and maxillofacial surgery (OMS) significantly depends on the accuracy of surgical navigation. In this article, a vision-based markerless surgical navigation system is developed to overcome the shortcomings in the currently available technologies. Registration methods both for patient and surgical instrument tracking are improved to increase navigation performance. For patient-image registration, we propose an efficient texture-less pose estimation method using only shape information. An innovative strategy is developed to effectively reject outliers and improve pose accuracy, which is the first attempt at introducing geometric matching information to guide PnP calculation. For surgical instrument tracking, a position-sensing marker is employed to achieve robust and convenient instrument localization with high accuracy. Experiments were conducted on the 3D-printed maxilla and mandible models to evaluate the navigation performance. Evaluation results validate the effectiveness of the proposed pose estimation method in improving pose accuracy for texture-less teeth. Besides, it is revealed that the position-sensing marker can be localized with high accuracy even under non-ideal visibility conditions, which expands the motion range of the instrument and decreases the size of the tool. The entire system has a sufficiently small target registration error. These experimental results have verified that the proposed surgical navigation system can provide practical guidance for OMS with satisfactory accuracy.