Computational imaging has always benefited from tools that modulate light along the many dimensions of its plenoptic function. This paper provides a practical architecture for achieving spatially varying spectral modulation using a liquid crystal phase spatial light modulator (SLM). The use of a phase SLM, however, results in strong optical aberrations due to the unintended phase modulation, thereby precluding spectral modulation at high spatial resolutions. To mitigate this, we provide a careful and systematic analysis of the aberrations arising out of phase SLMs for the purpose of spatially varying spectral modulation; this analysis results in a dual strategy of “good patterns” that minimize the optical aberrations and a deep restoration network that overcomes any residual aberrations. We show a number of unique operating points with our prototype including single- and multi-image hyperspectral imaging, material classification (fewer than two images), and dynamic spectral filtering at video rates.

In photography, as in other imaging areas, the aspect ratio of a picture is dictated by the aspect ratio of an image sensor. In this paper, we present a flexible and efficient lensless approach to overcome aspect ratio dependency. We introduce a theory of coded aperture imaging when the aspect ratio of the scene is mismatched with the aspect ratio of the sensor. Using our prototype, we demonstrate how our theoretical results can be effectively applied for ultra-wide imaging with a conventional image sensor, and for normal aspect ratio imaging with an ultra-wide sensor. We use the latter application to perform high frame rate imaging with a rolling shutter sensor by dividing the sensor area into multiple narrow sub-areas along the rolling shutter direction.

User identification from hand images only is still a challenging task. In this paper, we propose a new biometric identification system based solely on a skin patch from a multispectral image. The system is utilizing a novel modified 3D CNN architecture which is taking advantage of multispectral data. We demonstrate the application of our system for the example of human identification from multispectral images of hands. To the best of our knowledge, this paper is the first to describe a pose-invariant and robust to overlapping real-time human identification system using hands. Additionally, we provide a framework to optimize the required spectral bands for the given spatial resolution limitations.

We attempted to produce an environment for education. Subjects such as mathematics or sciences are usually studied on a desk in a classroom. Our research goal is to allow students to study scientific contents more viscerally than existing studying methods by haptic interaction. To construct the environment, we have to make a user-friendly haptic interface. The study is described in two parts. The first part is defining what a useful haptic interface is. In this part, we focused on the grip of a haptic interface. SPIDAR-G is a haptic interface, which is manipulated by a grip with 8 strings.. Grip size is an important parameter for usability. We have found an optimal sphere size through SPIDAR-G usability testing. The other part is defining how teachers can use the interactive system with haptic interaction as a teaching aid. In this part , we focused on the interaction between two water molecules. First, we constructed an environment to feel Van der Waals force as well as electrostatic force with haptic interaction. Then, we observe the effectiveness of the environment when used by a class of students.