Paper Info
Title
An Optimal Algorithm For Reconstructing Images From Binary Measurements
Abstract
We have studied a camera with a very large number of binary pixels referred to as the gigavision camera [ 1] or the gigapixel digital film camera [2, 3]. Potential advantages of this new camera design include improved dynamic range, thanks to its logarithmic sensor response curve, and reduced exposure time in low light conditions, due to its highly sensitive photon detection mechanism.We use maximum likelihood estimator (MLE) to reconstruct a high quality conventional image from the binary sensor measurements of the gigavision camera. We prove that when the threshold T is "1", the negative log-likelihood function is a convex function. Therefore, optimal solution can be achieved using convex optimization. Base on filter bank techniques, fast algorithms are given for computing the gradient and the multiplication of a vector and Hessian matrix of the negative log-likelihood function. We show that with a minor change, our algorithm also works for estimating conventional images from multiple binary images.Numerical experiments with synthetic 1-D signals and images verify the effectiveness and quality of the proposed algorithm. Experimental results also show that estimation performance can be improved by increasing the oversampling factor or the number of binary images.
Year
DOI
Venue
2010
10.1117/12.850887
COMPUTATIONAL IMAGING VIII
Keywords
Field
DocType
Computational photography, high dynamic range imaging, low light level imaging, the gigavision camera, digital film, photon-limited imaging
Binary image,Hessian matrix,Artificial intelligence,Binary number,Iterative reconstruction,Computer vision,Computational photography,Optics,Algorithm,Pixel,Convex optimization,High-dynamic-range imaging,Physics
Conference
Volume
ISSN
Citations 
7533
0277-786X
2
PageRank 
References 
Authors
0.40
1
4
Name
Order
Citations
PageRank
Feng Yang18611.70
Yue M. Lu267760.17
Luciano Sbaiz38411.42
Martin Vetterli4139262397.68