17,359 research outputs found
Multi-channel coded-aperture photography
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 87-89).This thesis describes the multi-channel coded-aperture photography, a modified camera system that can extract an all-focus image of the scene along with a depth estimate over the scene. The modification consists of inserting a set of patterned color filters into the aperture of the camera lens. This work generalizes the previous research on a single-channel coded aperture, by deploying distinct filters in the three primary color channels, in order to cope better with the effect of a Bayer filter and to exploit the correlation among the channels. We derive the model and algorithms for the multi-channel coded aperture, comparing the simulated performance of the reconstruction algorithm against that of the original single-channel coded aperture. We also demonstrate a physical prototype, discussing the challenges arising from the use of multiple filters. We provide a comparison with the single-channel coded aperture in performance, and present results on several scenes of cluttered objects at various depths.by Jongmin Baek.M.Eng
Light Field Blind Motion Deblurring
We study the problem of deblurring light fields of general 3D scenes captured
under 3D camera motion and present both theoretical and practical
contributions. By analyzing the motion-blurred light field in the primal and
Fourier domains, we develop intuition into the effects of camera motion on the
light field, show the advantages of capturing a 4D light field instead of a
conventional 2D image for motion deblurring, and derive simple methods of
motion deblurring in certain cases. We then present an algorithm to blindly
deblur light fields of general scenes without any estimation of scene geometry,
and demonstrate that we can recover both the sharp light field and the 3D
camera motion path of real and synthetically-blurred light fields.Comment: To be presented at CVPR 201
Depth Estimation Through a Generative Model of Light Field Synthesis
Light field photography captures rich structural information that may
facilitate a number of traditional image processing and computer vision tasks.
A crucial ingredient in such endeavors is accurate depth recovery. We present a
novel framework that allows the recovery of a high quality continuous depth map
from light field data. To this end we propose a generative model of a light
field that is fully parametrized by its corresponding depth map. The model
allows for the integration of powerful regularization techniques such as a
non-local means prior, facilitating accurate depth map estimation.Comment: German Conference on Pattern Recognition (GCPR) 201
Pixel level optical-transfer-function design based on the surface-wave-interferometry aperture
The design of optical transfer function (OTF) is of significant importance for optical information processing in various imaging and vision systems. Typically, OTF design relies on sophisticated bulk optical arrangement in the light path of the optical systems. In this letter, we demonstrate a surface-wave-interferometry aperture (SWIA) that can be directly incorporated onto optical sensors to accomplish OTF design on the pixel level. The whole aperture design is based on the bull’s eye structure. It composes of a central hole (diameter of 300 nm) and periodic groove (period of 560 nm) on a 340 nm thick gold layer. We show, with both simulation and experiment, that different types of optical transfer functions (notch, highpass and lowpass filter) can be achieved by manipulating the interference between the direct transmission of the central hole and the surface wave (SW) component induced from the periodic groove. Pixel level OTF design provides a low-cost, ultra robust, highly compact method for numerous applications such as optofluidic microscopy, wavefront detection, darkfield imaging, and computational photography
Aperture Supervision for Monocular Depth Estimation
We present a novel method to train machine learning algorithms to estimate
scene depths from a single image, by using the information provided by a
camera's aperture as supervision. Prior works use a depth sensor's outputs or
images of the same scene from alternate viewpoints as supervision, while our
method instead uses images from the same viewpoint taken with a varying camera
aperture. To enable learning algorithms to use aperture effects as supervision,
we introduce two differentiable aperture rendering functions that use the input
image and predicted depths to simulate the depth-of-field effects caused by
real camera apertures. We train a monocular depth estimation network end-to-end
to predict the scene depths that best explain these finite aperture images as
defocus-blurred renderings of the input all-in-focus image.Comment: To appear at CVPR 2018 (updated to camera ready version
Learning Wavefront Coding for Extended Depth of Field Imaging
Depth of field is an important factor of imaging systems that highly affects
the quality of the acquired spatial information. Extended depth of field (EDoF)
imaging is a challenging ill-posed problem and has been extensively addressed
in the literature. We propose a computational imaging approach for EDoF, where
we employ wavefront coding via a diffractive optical element (DOE) and we
achieve deblurring through a convolutional neural network. Thanks to the
end-to-end differentiable modeling of optical image formation and computational
post-processing, we jointly optimize the optical design, i.e., DOE, and the
deblurring through standard gradient descent methods. Based on the properties
of the underlying refractive lens and the desired EDoF range, we provide an
analytical expression for the search space of the DOE, which is instrumental in
the convergence of the end-to-end network. We achieve superior EDoF imaging
performance compared to the state of the art, where we demonstrate results with
minimal artifacts in various scenarios, including deep 3D scenes and broadband
imaging
Variational Disparity Estimation Framework for Plenoptic Image
This paper presents a computational framework for accurately estimating the
disparity map of plenoptic images. The proposed framework is based on the
variational principle and provides intrinsic sub-pixel precision. The
light-field motion tensor introduced in the framework allows us to combine
advanced robust data terms as well as provides explicit treatments for
different color channels. A warping strategy is embedded in our framework for
tackling the large displacement problem. We also show that by applying a simple
regularization term and a guided median filtering, the accuracy of displacement
field at occluded area could be greatly enhanced. We demonstrate the excellent
performance of the proposed framework by intensive comparisons with the Lytro
software and contemporary approaches on both synthetic and real-world datasets
Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging
A variety of techniques such as light field, structured illumination, and
time-of-flight (TOF) are commonly used for depth acquisition in consumer
imaging, robotics and many other applications. Unfortunately, each technique
suffers from its individual limitations preventing robust depth sensing. In
this paper, we explore the strengths and weaknesses of combining light field
and time-of-flight imaging, particularly the feasibility of an on-chip
implementation as a single hybrid depth sensor. We refer to this combination as
depth field imaging. Depth fields combine light field advantages such as
synthetic aperture refocusing with TOF imaging advantages such as high depth
resolution and coded signal processing to resolve multipath interference. We
show applications including synthesizing virtual apertures for TOF imaging,
improved depth mapping through partial and scattering occluders, and single
frequency TOF phase unwrapping. Utilizing space, angle, and temporal coding,
depth fields can improve depth sensing in the wild and generate new insights
into the dimensions of light's plenoptic function.Comment: 9 pages, 8 figures, Accepted to 3DV 201
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