Publication Type : Conference Proceedings
Thematic Areas : Center for Computational Engineering and Networking (CEN)
Publisher : 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013
Source : 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013, IEEE, Kochi, Kerala, p.729-736 (2013)
Url : http://www.scopus.com/inward/record.url?eid=2-s2.0-84880107287&partnerID=40&md5=fba2fe433e431ced867af960273f3a1c
ISBN : 9781467350891
Keywords : Algorithms, Channel estimation, Communication, Compressed sensing, De-noising, Gaussian noise (electronic), Image denoising, Image matching, Image reconstruction, Linear algebra, Orthogonal matching pursuit, Over-complete dictionaries, Sparsity, Wavelet thresholding
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Computer Science, Electronics and Communication
Verified : Yes
Year : 2013
Abstract : Signal or image reconstruction has now become a common task in many applications. According to linear algebra perspective, the number of measurements made or the number of samples taken for reconstruction must be greater than or equal to the dimension of signal or image. Also reconstruction follows the Shanon's sampling theorem which is based on the Nyquist sampling rate. The reconstruction of a signal or image using the principle of compressed sensing is an exception which makes use of only few number of samples which is below the sampling limit. Compressive sensing also known as sparse recovery aims to provide a better data acquisition and reduces computational complexities that occur while solving problems. The main goal of this paper is to provide clear and easy way to understand one of the compressed sensing greedy algorithm called Orthogonal Matching Pursuit (OMP). The OMP algorithm involves the concept of overcomplete dictionary that is formulated based on different thresholding methods. The proposed method gives the simplified approach for image denoising by using OMP only. The experiment is performed on few standard image data set simulated with different types of noises such as Gaussian noise, salt and pepper noise, exponential noise and Poisson noise. The performance of the proposed method is evaluated based on the image quality metric, Peak Signal-to-Noise Ratio (PSNR). © 2013 IEEE.
Cite this Research Publication : Suchithra M., Sukanya, P., Prabha, P., Sikha O. K., Sowmya, and Dr. Soman K. P., “An experimental study on application of orthogonal matching pursuit algorithm for image denoising”, 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. IEEE, Kochi, Kerala, pp. 729-736, 2013.