A joint reconstruction algorithm for multi-view compressed imaging

Chang, Kan; Qin, Tuanfa; Xu, Wenbo; Men, Aidong

doi:10.1109/iscas.2013.6571822

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Compared with model (6), the first two terms in the proposed model can prevent finding a non-reliable result, especially when the residual of a frame is not sparse enough. Containing two TV norm terms and one l 1 norm term, problem (8) is even harder to solve than our previously proposed models in [12] and [13], which are used for multiview CS imaging. Hence, we propose a new algorithm called RA-CSVCR to get the solution of (8), and also develop its extended version RA-CSVCR-EXT.…”

Section: The Proposed Reconstruction Modelmentioning

confidence: 93%

Reconstruction of compressed-sensed video using compound regularization

Chang

Qin

Tang

et al. 2014

2014 IEEE International Conference on Multimedia and Expo (ICME)

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This paper introduces a novel reconstruction model with compound regularization to recover compressed-sensed video sequences. For a target frame, the compound regularization consists of total variation (TV) norm of the frame, l 1 norm of the frame in a certain transform domain, and TV norm of the residual between the frame and its prediction. The first two terms in the compound regularization are used to describe image characteristics, while the third term exploits inter-frame correlation within video sequences. To solve the minimization problem, a new splitting objective function is considered, and it is divided into sub-problems that are easy to solve. In addition, bivariate shrinkage method is integrated into the proposed algorithm so that high quality of reconstruction results can be guaranteed. Experimental results show that the proposed algorithms are substantially superior to state-ofthe-art reconstruction methods.

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Section: The Proposed Reconstruction Modelmentioning

confidence: 93%

Reconstruction of compressed-sensed video using compound regularization

Chang

Qin

Tang

et al. 2014

2014 IEEE International Conference on Multimedia and Expo (ICME)

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“…The final reconstruction is aided by calculating the residuals among the side information and the original view. Also, in [16], a DC/DE-based joint reconstruction scheme is proposed. The proximal gradient method is adopted by the proposed scheme to resolve the optimization problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, the scheme is exposed to prediction errors. The schemes proposed in [13][14][15][16][17][18][19] are mostly based on DC/MC and DE/ME prediction methods. In such a scheme, precise predictions are hard to achieve if basic transformation (translation/affine) models are used.…”

Section: Literature Reviewmentioning

confidence: 99%

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Block Compressive Sensing Single-View Video Reconstruction Using Joint Decoding Framework for Low Power Real Time Applications

et al. 2020

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Several real-time visual monitoring applications such as surveillance, mental state monitoring, driver drowsiness and patient care, require equipping high-quality cameras with wireless sensors to form visual sensors and this creates an enormous amount of data that has to be managed and transmitted at the sensor node. Moreover, as the sensor nodes are battery-operated, power utilization is one of the key concerns that must be considered. One solution to this issue is to reduce the amount of data that has to be transmitted using specific compression techniques. The conventional compression standards are based on complex encoders (which require high processing power) and simple decoders and thus are not pertinent for battery-operated applications, i.e., VSN (primitive hardware). In contrast, compressive sensing (CS) a distributive source coding mechanism, has transformed the standard coding mechanism and is based on the idea of a simple encoder (i.e., transmitting fewer data-low processing requirements) and a complex decoder and is considered a better option for VSN applications. In this paper, a CS-based joint decoding (JD) framework using frame prediction (using keyframes) and residual reconstruction for single-view video is proposed. The idea is to exploit the redundancies present in the key and non-key frames to produce side information to refine the non-key frames’ quality. The proposed method consists of two main steps: frame prediction and residual reconstruction. The final reconstruction is performed by adding a residual frame with the predicted frame. The proposed scheme was validated on various arrangements. The association among correlated frames and compression performance is also analyzed. Various arrangements of the frames have been studied to select the one that produces better results. The comprehensive experimental analysis proves that the proposed JD method performs notably better than the independent block compressive sensing scheme at different subrates for various video sequences with low, moderate and high motion contents. Also, the proposed scheme outperforms the conventional CS video reconstruction schemes at lower subrates. Further, the proposed scheme was quantized and compared with conventional video codecs (DISCOVER, H-263, H264) at various bitrates to evaluate its efficiency (rate-distortion, encoding, decoding).

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