2 research outputs found

    Widening Viewing Angles of Automultiscopic Displays using Refractive Inserts

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    Widening the view angle of auto-multiscopic display, denoising low brightness light field data and 3D reconstruction with delicate details

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    This doctoral thesis will present the results of my work into widening the viewing angle of the auto-multiscopic display, denoising light filed data the enhancement of captured light filed data captured in low light circumstance, and the attempts on reconstructing the subject surface with delicate details from microscopy image sets. The automultiscopic displays carefully control the distribution of emitted light over space, direction (angle) and time so that even a static image displayed can encode parallax across viewing directions (light field). This allows simultaneous observation by multiple viewers, each perceiving 3D from their own (correct) perspective. Currently, the illusion can only be effectively maintained over a narrow range of viewing angles. We propose and analyze a simple solution to widen the range of viewing angles for automultiscopic displays that use parallax barriers. We insert a refractive medium, with a high refractive index, between the display and parallax barriers. The inserted medium warps the exitant lightfield in a way that increases the potential viewing angle. We analyze the consequences of this warp and build a prototype with a 93% increase in the effective viewing angle. Additionally, we developed an integral images synthesis method that can address the refraction introduced by the inserted medium efficiently without the use of ray tracing. Capturing light field image with a short exposure time is preferable for eliminating the motion blur but it also leads to low brightness in a low light environment, which results in a low signal noise ratio. Most light field denoising methods apply regular 2D image denoising method to the sub-aperture images of a 4D light field directly, but it is not suitable for focused light field data whose sub-aperture image resolution is too low to be applied regular denoising methods. Therefore, we propose a deep learning denoising method based on micro lens images of focused light field to denoise the depth map and the original micro lens image set simultaneously, and achieved high quality total focused images from the low focused light field data. In areas like digital museum, remote researching, 3D reconstruction with delicate details of subjects is desired and technology like 3D reconstruction based on macro photography has been used successfully for various purposes. We intend to push it further by using microscope rather than macro lens, which is supposed to be able to capture the microscopy level details of the subject. We design and implement a scanning method which is able to capture microscopy image set from a curve surface based on robotic arm, and the 3D reconstruction method suitable for the microscopy image set
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