Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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Compressed sensing is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from fewer samples than required by the criterion. In compressed sensing, data is compressed and converted into fewer measurements and transmitted through wireless channel which is reconstructed at the receiver. Since very few samples are used for reconstruction, there are possibilities of degradation in quality of reconstruction. Unlike traditional methods, enhancement can now be done using the recent technique of compressed sensing by embedding image enhancement techniques like edge detection, histogram, filtering and their combinations with CS recovery procedure. This work proposes such a method by binding the image enhancement techniques along with the compressed sensing process. Filter of Gaussian filter (FGF), a combinational filter proposed in this study enables an increase in PSNR of 1 dB when compared to other filtering techniques besides using least number of measurements and maintaining minimum time consumption. The runtime difference with and without the FGF is ~ 3 s, which is affordable even in hardware with minimum specifications. Real time experimentation of embedded enhancement CS was carried out in WINGZ board to prove the feasibility of enhanced recovery process with lower end hardware. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

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Hardware Implementation of Enhancement Embedded Reduced Runtime Recovery Algorithm for Compressively Sensed Images

Compressed sensing is widely applied for compression and reconstruction of images and videos by projecting the pixel values to smaller dimensional measurements. These measurements are reconstructed at the receiver using various reconstruction procedures. Greedy algorithms are often used for such recovery. These solve the least squares problem to find the best match with minimum error. This is a time consuming and complex process, giving rise to a trade-off between reconstruction performance and algorithmic performance. This work proposes a non-iterative method, viz., non-iterative pseudo inverse based recovery algorithm (NIPIRA), for reconstruction of compressively sensed images and videos with small complexity and time consumption, provided the reconstruction quality is maintained. NIPIRA gives a minimum PSNR of 32 dB for very few measurements (M/N = 0.3125) and accuracy of above 97%. There is more than 92% of decrease in elapsed time compared with other iterative algorithms. NIPIRA is tested for its performance with respect to many other objective measures as well. The complexity of NIPIRA is s times less than existing recovery algorithms. © 2016 Elsevier GmbH