432 research outputs found
Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks
Light field imaging extends the traditional photography by capturing both
spatial and angular distribution of light, which enables new capabilities,
including post-capture refocusing, post-capture aperture control, and depth
estimation from a single shot. Micro-lens array (MLA) based light field cameras
offer a cost-effective approach to capture light field. A major drawback of MLA
based light field cameras is low spatial resolution, which is due to the fact
that a single image sensor is shared to capture both spatial and angular
information. In this paper, we present a learning based light field enhancement
approach. Both spatial and angular resolution of captured light field is
enhanced using convolutional neural networks. The proposed method is tested
with real light field data captured with a Lytro light field camera, clearly
demonstrating spatial and angular resolution improvement
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
A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras
This paper proposes an observer for generating depth maps of a scene from a
sequence of measurements acquired by a two-plane light-field (plenoptic)
camera. The observer is based on a gradient-descent methodology. The use of
motion allows for estimation of depth maps where the scene contains
insufficient texture for static estimation methods to work. A rigourous
analysis of stability of the observer error is provided, and the observer is
tested in simulation, demonstrating convergence behaviour.Comment: Full version of paper submitted to CDC 2018. 11 pages. 12 figure
Deep Depth From Focus
Depth from focus (DFF) is one of the classical ill-posed inverse problems in
computer vision. Most approaches recover the depth at each pixel based on the
focal setting which exhibits maximal sharpness. Yet, it is not obvious how to
reliably estimate the sharpness level, particularly in low-textured areas. In
this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end
learning approach to this problem. One of the main challenges we face is the
hunger for data of deep neural networks. In order to obtain a significant
amount of focal stacks with corresponding groundtruth depth, we propose to
leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us
to digitally create focal stacks of varying sizes. Compared to existing
benchmarks our dataset is 25 times larger, enabling the use of machine learning
for this inverse problem. We compare our results with state-of-the-art DFF
methods and we also analyze the effect of several key deep architectural
components. These experiments show that our proposed method `DDFFNet' achieves
state-of-the-art performance in all scenes, reducing depth error by more than
75% compared to the classical DFF methods.Comment: accepted to Asian Conference on Computer Vision (ACCV) 201
Geometric Inference with Microlens Arrays
This dissertation explores an alternative to traditional fiducial markers where geometric
information is inferred from the observed position of 3D points seen in an image. We offer an alternative approach which enables geometric inference based on the relative orientation
of markers in an image. We present markers fabricated from microlenses whose appearance
changes depending on the marker\u27s orientation relative to the camera. First, we show how
to manufacture and calibrate chromo-coding lenticular arrays to create a known relationship
between the observed hue and orientation of the array. Second, we use 2 small chromo-coding lenticular arrays to estimate the pose of an object. Third, we use 3 large chromo-coding lenticular arrays to calibrate a camera with a single image. Finally, we create another type of fiducial marker from lenslet arrays that encode orientation with discrete black and white appearances. Collectively, these approaches oer new opportunities for pose estimation and camera calibration that are relevant for robotics, virtual reality, and augmented reality
Depth and All-in-Focus Image Estimation in Synthetic Aperture Integral Imaging Under Partial Occlusions
A common assumption in the integral imaging reconstruction is that a pixel will be photo-consistent if all viewpoints observed by the different cameras converge at a single point when focusing at the proper depth. However, the presence of occlusions between objects in the scene prevents this from being fulfilled. In this paper, a novel depth and all-in focus image estimation method is presented, based on a photo-consistency measure that uses the median criterion in relation to the elemental images. The interest of this approach is to find a solution to detect which camera correctly sees the partially occluded object at a certain depth and allows for a precise solution to the object depth. In addition, a robust solution is proposed to detect the boundary limits between partially occluded objects, which are subsequently used during the regularization depth estimation process. The experimental results show that the proposed method outperforms other state-of-the-art depth estimation methods in a synthetic aperture integral imaging framework
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