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In this paper, we propose a novel deep neural network model that reconstructs a high dynamic range (HDR) image from a single low dynamic range (LDR) image. The proposed model is based on a convolutional neural network composed of dilated convolutional layers, and infers LDR images with various exposures and illumination from a single LDR image of the same scene. Then, the final HDR image can be formed by merging these inference results. It is relatively easy for the proposed method to find the mapping between the LDR and an HDR with a different bit depth because of the chaining structure inferring the relationship between the LDR images with brighter (or darker) exposures from a given LDR image. The method not only extends the range, but also has the advantage of restoring the light information of the actual physical world. For the HDR images obtained by the proposed method, the HDR-VDP2 Q score, which is the most popular evaluation metric for HDR images, was 56.36 for a display with a 1920×1200 resolution, which is an improvement of 6 compared with the scores of conventional algorithms. In addition, when comparing the peak signal-to-noise ratio values for tone mapped HDR images generated by the proposed and conventional algorithms, the average value obtained by the proposed algorithm is 30.86 dB, which is 10 dB higher than those obtained by the conventional algorithms.

In this paper, we propose a Charuco board-based omnidirectional camera calibration method to solve the problem of conventional methods requiring overly complicated calibration procedures. Specifically, the proposed method can easily and precisely provide two-dimensional and three-dimensional coordinates of patterned feature points by arranging the omnidirectional camera in the Charuco board-based cube structure. Then, using the coordinate information of the feature points, an intrinsic calibration of each camera constituting the omnidirectional camera can be performed by estimating the perspective projection matrix. Furthermore, without an additional calibration structure, an extrinsic calibration of each camera can be performed, even though only part of the calibration structure is included in the captured image. Compared to conventional methods, the proposed method exhibits increased reliability, because it does not require additional adjustments to the mirror angle or the positions of several pattern boards. Moreover, the proposed method calibrates independently, regardless of the number of cameras comprising the omnidirectional camera or the camera rig structure. In the experimental results, for the intrinsic parameters, the proposed method yielded an average reprojection error of 0.37 pixels, which was better than that of conventional methods. For the extrinsic parameters, the proposed method had a mean absolute error of 0.90° for rotation displacement and a mean absolute error of 1.32 mm for translation displacement.

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Because the interest in virtual reality (VR) has increased recently, studies on head-mounted displays (HMDs) have been actively conducted. However, HMD causes motion sickness and dizziness to the user, who is most affected by motion-to-photon latency. Therefore, equipment for measuring and quantifying this occurrence is very necessary. This paper proposes a novel system to measure and visualize the time sequential motion-to-photon latency in real time for HMDs. Conventional motion-to-photon latency measurement methods can measure the latency only at the beginning of the physical motion. On the other hand, the proposed method can measure the latency in real time at every input time. Specifically, it generates the rotation data with intensity levels of pixels on the measurement area, and it can obtain the motion-to-photon latency data in all temporal ranges. Concurrently, encoders measure the actual motion from a motion generator designed to control the actual posture of the HMD device. The proposed system conducts a comparison between two motions from encoders and the output image on a display. Finally, it calculates the motion-to-photon latency for all time points. The experiment shows that the latency increases from a minimum of 46.55 ms to a maximum of 154.63 ms according to the workload levels.