Video compressive sensing using spatial domain sparsity

Zheng, Jing; Jacobs, Eddie L.

doi:10.1117/1.3206733

Cited by 51 publications

(40 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can result in long reconstruction times measured in minutes or hours. Recent developments make progress toward compressive video reconstruction [12][13][14][15][16][17][18], with emphasis on developing methods to reduce computational time [19,20]; however, in this paper we concentrate on compressive techniques that do not require computationally expensive reconstruction and are therefore able to provide near video frame rates.…”

Section: Introductionmentioning

confidence: 99%

Single-pixel infrared and visible microscope

Radwell¹,

Mitchell²,

Gibson³

et al. 2014

Optica

378

189

View full text Add to dashboard Cite

Microscopy is an essential tool in a huge range of research areas. Until now, microscopy has been largely restricted to imaging in the visible region of the electromagnetic spectrum. Here we present a microscope system that uses single-pixel imaging techniques to produce images simultaneously in the visible and shortwave infrared. We apply our microscope to the inspection of various objects, including a silicon CMOS sensor, highlighting the complementarity of the visible and shortwave infrared wavebands. The system is capable of producing images with resolutions between 32 × 32 and 128 × 128 pixels at corresponding frame rates between 10 and 0.6 Hz. We introduce a compressive technique that does not require postprocessing, resulting in a predicted frame rate increase by a factor 8 from a compressive ratio of 12.5% with only 28% relative error.

show abstract

Section: Introductionmentioning

confidence: 99%

Single-pixel infrared and visible microscope

Radwell¹,

Mitchell²,

Gibson³

et al. 2014

Optica

378

189

View full text Add to dashboard Cite

show abstract

“…However, signal reconstruction and sparse representation are designed as independent tasks in [7,8,28], and then, it has a negative impact in terms of consuming resources, since the sparse coefficient calculation has already been included in the process of dictionary learning. Zheng and Jacobs [11] presented a differencing method in order to take advantage of the spatial redundancy between neighboring frames, but it is only suitable for sequences that have small spatial changes. In [12], Ma et al proposed a modified approximate message passing algorithm to recover the undersampled videos, by using the 3D dual-tree complex wavelet transform with some correlation noise.…”

Section: Related Workmentioning

confidence: 99%

Optimal-correlation-based reconstruction for distributed compressed video sensing

Liu

Song

Fang

et al. 2015

Journal of Visual Communication and Image Representation

View full text Add to dashboard Cite

“…Stankovic [30] and Prades-Nebot et al [25] divided each frame into non-overlapping blocks and approximate each block by a linear combination of blocks in previously transmitted frames the CS decoding process. Zheng and Jacobs [36] proposed a video compressive sensing method using spatial domain sparsity, where key frames or reference frames are fully sampled and CS measurements are applied to the difference between the successive frames and the other frames. Xu et al [33] incorporated a user attention model with visual rhythm analysis in CS video processing which can automatically determine regions of interest.…”

Section: A Related Workmentioning

confidence: 99%

Compressive Video Sampling With Approximate Message Passing Decoding

Plonka

Hussaini

2012

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

In this paper, we apply compressed sensing to video compression. Compressed sensing (CS) techniques exploit the observation that one needs much fewer random measurements than given by the Shannon-Nyquist sampling theory to recover an object if this object is compressible (i.e., sparse in the spatial domain or in a transform domain). In the CS framework, we can achieve sensing, compression and denoising simultaneously. We propose a fast and simple online encoding by application of pseudo-random downsampling of the two-dimensional fast Fourier transform to video frames. For off-line decoding, we apply a modification of the recently proposed approximate message passing (AMP) algorithm. The AMP method has been derived using the statistical concept of 'state evolution', and it has been shown to considerably accelerate the convergence rate in special CS-decoding applications. We shall prove that the AMP method can be rewritten as a forward-backward splitting algorithm. This new representation enables us to give conditions that ensure convergence of the AMP method and to modify the algorithm in order to achieve higher robustness. The success of reconstruction methods for video decoding also essentially depends on the chosen transform, where sparsity of the video signals is assumed. We propose to incorporate the 3D dual-tree complex wavelet transform that possesses sufficiently good properties of directional selectivity and shift invariance while being computationally less expensive and less redundant than other directional 3D wavelet transforms.

show abstract

Video compressive sensing using spatial domain sparsity

Cited by 51 publications

References 24 publications

Single-pixel infrared and visible microscope

Single-pixel infrared and visible microscope

Optimal-correlation-based reconstruction for distributed compressed video sensing

Compressive Video Sampling With Approximate Message Passing Decoding

Contact Info

Product

Resources

About