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In this study, we propose view interpolation networks to reproduce changes in the brightness of an object's surface depending on the viewing direction, which is important in reproducing the material appearance of a real object. We use an original and a modified version of U-Net for image transformation. The networks were trained to generate images from intermediate viewpoints of four cameras placed at the corners of a square. We conducted an experiment with three different combinations of methods and training data formats. We found that it is best to input the coordinates of the viewpoints together with the four camera images and to use images from random viewpoints as the training data.

Multi-path interference causes depth errors in indirect time-of-flight (ToF) cameras. In this paper, resolving multi-path interference caused by surface reflections using a multi-tap macro-pixel computational CMOS image sensor is demonstrated. The imaging area is implemented by an array of macro-pixels composed of four subpixels embodied by a four-tap lateral electric field charge modulator (LEFM). This sensor can simultaneously acquire 16 images for different temporal shutters. This method can reproduce more than 16 images based on compressive sensing with multi-frequency shutters and sub-clock shifting. In simulations, an object was placed 16 m away from the sensor, and the depth of an interference object was varied from 1 to 32 m in 1 m steps. The two reflections were separated in two stages: coarse estimation based on a compressive sensing solver and refinement by a nonlinear search to investigate the potential of our sensor. Relative standard deviation (precision) and relative mean error (accuracy) were evaluated under the influence of photon shot noise. The proposed method was verified using a prototype multi-tap macro-pixel computational CMOS image sensor in single-path and dual-path situations. In the experiment, an acrylic plate was placed 1 m or 2 m and a mirror 9.3 m from the sensor.

In our previous work, we proposed a user interface in which a user wears a projector and a depth camera on his or her head and performs touch operations on an image projected on a flat surface. By using the head-mounted projector, images are always projected in front of the user in the direction of the user’s gaze. The image to be projected is changed according to the user’s head pose so as to fix the superimposed image on the surface, which realizes a large effective screen size. In this paper, we conducted an experiment for evaluating the accuracy of registration by measuring the positional and rotational errors between the real world and the superimposed image using our experimental system. As a result, the mean absolute errors of translation were about 10 mm when the user stopped his head, and the delay was estimated to be about 0.2 s. We also discuss the limitations of our prototype and show the direction of future development.

A probabilistic method to remove the random telegraph signal (RTS) noise and to increase the signal level is proposed, and was verified by simulation based on measured real sensor noise. Although semi-photon-counting-level (SPCL) ultra-low noise complementary-metal-oxide-semiconductor (CMOS) image sensors (CISs) with high conversion gain pixels have emerged, they still suffer from huge RTS noise, which is inherent to the CISs. The proposed method utilizes a multi-aperture (MA) camera that is composed of multiple sets of an SPCL CIS and a moderately fast and compact imaging lens to emulate a very fast single lens. Due to the redundancy of the MA camera, the RTS noise is removed by the maximum likelihood estimation where noise characteristics are modeled by the probability density distribution. In the proposed method, the photon shot noise is also relatively reduced because of the averaging effect, where the pixel values of all the multiple apertures are considered. An extremely low-light condition that the maximum number of electrons per aperture was the only 2 e − was simulated. PSNRs of a test image for simple averaging, selective averaging (our previous method), and the proposed method were 11.92 dB, 11.61 dB, and 13.14 dB, respectively. The selective averaging, which can remove RTS noise, was worse than the simple averaging because it ignores the pixels with RTS noise and photon shot noise was less improved. The simulation results showed that the proposed method provided the best noise reduction performance.