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