Framework for Image Denoising Employing Different Thresholding Techniques

Abstract

Background: Noise represents a lack of data in the image which can be removed using Image denoising. Image denoising can be achieved by Gaussian filtering, anisotropic filtering, wavelet Thresholding, etc. Objective: In this paper, authors have used Wavelet-based denoising because it can effectively remove both additive and multiplicative noise from images, and preserve fine details and edges in the image. Methods: The different thresholding techniques like Visu Shrink and Bayes Shrink for Hard Thresholding (HT) and Soft Thresholding (ST) employing different standard deviations ranging from 0.05-0.3 with a difference of 0.05 is used. Results: The peak signal-to-noise ratio (PSNR) is evaluated as a performance parameter. For grayscale images, the maximum value of PSNR is obtained as 29.483 dB while for RGB images, 34.324dB using Bayes Shrink considering ST at 0.05 variance is achieved. 2.2% improvement is observed for grayscale images while 8.6% improvement is observed for RGB images considering Bayes Shrink ST over Bayes Shrink HT. Conclusion: While comparing PSNR values of other Thresholding techniques, ST results better over HT. The PSNR values for images produced by Bayes Shrink are high which therefore states that the quality of reconstructed images is better for Bayes Shrink than Visu Shrink.