Compressive coded aperture superresolution image reconstruction

Marcia, Roummel F.; Willett, Rebecca

doi:10.1109/icassp.2008.4517739

Cited by 109 publications

(129 citation statements)

References 9 publications

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“…Wakin et al [4] used the single-pixel camera for video capturing by using the sparsity of 3D discrete wavelet transform (DWT) coefficients. Other structural features in videos, such as the temporal coherence among multiple frames, sparsity in 2D DWT, and multi-scale representations have also been used for video reconstruction [7,14,6].…”

Section: Related Workmentioning

confidence: 99%

Video from a single coded exposure photograph using a learned over-complete dictionary

Hitomi

Gupta

et al. 2011

2011 International Conference on Computer Vision

227

198

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Cameras face a fundamental tradeoff between the spatial and temporal resolution -digital still cameras can capture images with high spatial resolution, but most high-speed video cameras suffer from low spatial resolution. It is hard to overcome this tradeoff without incurring a significant increase in hardware costs. In this paper, we propose techniques for sampling, representing and reconstructing the space-time volume in order to overcome this tradeoff. Our approach has two important distinctions compared to previous works: (1) we achieve sparse representation of videos by learning an over-complete dictionary on video patches, and (2) we adhere to practical constraints on sampling scheme which is imposed by architectures of present image sensor devices. Consequently, our sampling scheme can be implemented on image sensors by making a straightforward modification to the control unit. To demonstrate the power of our approach, we have implemented a prototype imaging system with per-pixel coded exposure control using a liquid crystal on silicon (LCoS) device. Using both simulations and experiments on a wide range of scenes, we show that our method can effectively reconstruct a video from a single image maintaining high spatial resolution.

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Section: Related Workmentioning

confidence: 99%

Video from a single coded exposure photograph using a learned over-complete dictionary

Hitomi

Gupta

et al. 2011

2011 International Conference on Computer Vision

227

198

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show abstract

“…This approach overcomes previous approaches such as [13] where the reconstruction of a frame depends only on the individual frame's sparsity without taking into account any temporal motion. It is also better than using inter-frame difference [20] which is insufficient for removing temporal redundancies.…”

Section: Fig 1 System Block Diagram Of Video Coding Scheme Proposedmentioning

confidence: 99%

Compressive Video Coding: A Review of the State-Of-The-Art

Baig¹,

Lai²,

Punchihewa³

2012

Video Compression

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“…In the general case, a common and suitable criterion for A is to be statistically incoherent with any fixed signal representation, which means that the bases of the measurement and sparse-representation domains of the signal are uncorrelated with overwhelming probability [2]. This property has been shown theoretically to strictly hold for matrices consisting of independent and identically distributed (iid) Gaussian random entries [3], [6], and also to nearly hold for other random-matrix ensembles [1], [4], [5], [14], [15]. In this work, we resort to an experimental validation of our measurement matrix for binary compressed sensing.…”

Section: Connection With Compressed Sensingmentioning

confidence: 99%

Binary Compressed Imaging

Bourquard

Unser

2013

IEEE Trans. on Image Process.

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Abstract-Compressed sensing can substantially reduce the number of samples required for conventional signal acquisition at the expense of an additional reconstruction procedure. It also provides robust reconstruction when using quantized measurements, including in the one-bit setting. In this paper, our goal is to design a framework for binary compressed sensing that is adapted to images. Accordingly, we propose an acquisition and reconstruction approach that complies with the high dimensionality of image data and that provides reconstructions of satisfactory visual quality. Our forward model describes data acquisition and follows physical principles. It entails a series of random convolutions performed optically followed by sampling and binary thresholding. The binary samples that are obtained can be either measured or ignored according to predefined functions. Based on these measurements, we then express our reconstruction problem as the minimization of a compound convex cost that enforces the consistency of the solution with the available binary data under total-variation regularization. Finally, we derive an efficient reconstruction algorithm relying on convex-optimization principles. We conduct several experiments on standard images and demonstrate the practical interest of our approach.

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Compressive coded aperture superresolution image reconstruction

Cited by 109 publications

References 9 publications

Video from a single coded exposure photograph using a learned over-complete dictionary

Video from a single coded exposure photograph using a learned over-complete dictionary

Compressive Video Coding: A Review of the State-Of-The-Art

Binary Compressed Imaging

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