STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Microelectronics and Solid State Devices

Design Simulation and Assessment of Image De-noising through Improved Median Filters Based Sub band Decomposition

  1. Kanika Shrivastava
  2. Dr.Vikas Soni
  3. Jitendra K Yadvendra

Abstract

The quality of an image sequence can degrade due to a number of causes, such as noise in the recording, transmission, or scanning. Additionally to enhancing the visual appeal, filtering offers the advantage of facilitating subsequent image processing tasks like coding, interpretation, or analysis. While attempting to restore a perfect image from a faulty copy, noise reduction is a top concern. Because of this, there isn't a single strategy that can be utilized for everything. However, in terms of performance, linear filters are unrivalled for eliminating Gaussian signal noise. Photographs can show noise. Linear filters are triggered by certain frequencies while the signal and noise are distributed across a wide region. usually in an unsatisfactory way. On the other hand, denoising a picture by breaking it up into several scales is a regular occurrence in the field of image processing, similar to the process of dissecting an item. In other cases, the knowledge of artifacts is more ingrained in the segmented picture during image processing. Here, a brand-new method for denoising images is provided that removes various sorts of noise from an image using sub band decomposition based on a median filter. The wavelet decomposition and median transform are combined for the benefits of sub-band decomposition. In the lab, it has been noted that the noisy coefficients value is higher on the first scale but decreases with time due to multiresolution analysis on the next gradation, and so on. Since this was the case, we came up with the following solution: using multi-resolution coefficients to remove background noise while preserving the quality of the picture, and gradually reducing the threshold for each stage of the process to allow the de-noising operation to filter out the noise. There are a couple of noise-filled, jumbled images. This unique approach reduces noises including gaussian, speckle, and salt-and-pepper noises when compared to earlier methods utilizing the PSNR measure denoising methods based on wavelets. Experiments support the hypothesis. To show that a novel sub band decomposition method based on enhanced symmetric, bell-shaped, and cantered weighted median filters has been proposed. Under various noise conditions and varied performance factors, the method outperforms the traditional wavelet decomposition method.

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