Compressed Sensing Reconstruction based on Adaptive Scale Parameter using Texture Feature
D.M.Annie Brighty Christilin1, M. Safish Mary2

1D.M.Annie Brighty Christilin* Research Scholar, Manonmaniam Sundaranar University, Tirunelveli.
2Dr.M.Safish Mary Assistant Professor, Department of Computer Science, St.Xaviers College, Tirunelveli. 

Manuscript received on September 19, 2019. | Revised Manuscript received on 29 September, 2019. | Manuscript published on October 10, 2019. | PP: 5455-5461 | Volume-8 Issue-12, October 2019. | Retrieval Number: K22460981119/2019©BEIESP | DOI: 10.35940/ijitee.K2246.1081219
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Abstract: While taking an MRI scan, the patients cannot static for a long time during the motions; the image formation process can create artifacts that may reduce the image quality. The Compressed Sensing (CS) mechanism is employed to reconstruct the original image from the limited data given as the sparse matrix. Hence, CS can be utilized to reduce the acceleration time for an MRI scan considering the patient’s health. So the sensing method is implemented by a suitable projection matrix for reconstructing the sparse signals from a few numbers of measurements using Compressed Sensing. The CS guarantees the recovery of the original image with high probability based on random Gaussian projection matrices. However, sparse ternarius projections are more apt for the implementation of hardware. In this article, the proposed deep learning method is employed to obtain a very sparse ternary projection in Compressed Sensing. Compressed Sensing Reconstruction using an adaptive scale parameter based on the texture feature is used to improve the image quality. The two scaling factors αx and αy are assigned to specify the fixed scale for changing the improvement of the image quality. In the parameter using texture feature, the αx and αy are assigned to α as an adaptive scale based on texture feature. In the TACS-SDANN architecture, there are two layers namely the sensing layer which trains the projection matrix and a reconstruction layer which trains for non-linear sparse matrix continuously using Auto-encoder. Experimentally, the scaling factors are calculated on the training data to get the mean PeakSignal-to-Noise Ratio (PSNR) for improving the image quality. Hence a new deep network layer is employed to improve the image quality in this proposed method. Hence the consequence of the proposed method is compared with the SDANN method based on the mean Peak-Signal-to-Noise Ratio (PSNR) to check the image quality. From that comparisons, the TACS-SDANN architecture is proposed to yield a better performance.
Keywords: Compressed Sensing, Deep Learning, SDANN architecture, LBP Image, Sparse-LBP Ternary Projection.
Scope of the Article: Deep Learning