An experimental study on application of Orthogonal Matching Pursuit algorithm for image denoising

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).

Study of Image Enhancement Techniques in Image Processing: A Review

“…The Orthogonal Matching Pursuit (OMP) algorithm can be a best alternative for signal recovery. [5]. And other various approaches are used to recover the signal including OMP algorithm, Matching Pursuits [24], K-SVD algorithm which is used in a various fields like denoising, updating the dictionary and compressing image.…”

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