Implementation of Pixel Likeness Weighted Frame (Plwf) Filter Technique Based Digital Image Denoising for DSP Applications
K. MahaLakshmi1, Jaya Prakash S2
1Dr. K. MahaLakshmi, Professor. Department of Information Technology, Karpagam College of Engineering, Coimbatore.
2Jaya Prakash S, Associate Professor, Department of Computer Science and Engineering, Idhaya Engineering College for Women, Chinnasalem.
Manuscript received on 07 August 2019. | Revised Manuscript received on 15 August 2019. | Manuscript published on 30 September 2019. | PP: 6887-6894 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5846098319/2019©BEIESP | DOI: 10.35940/ijrte.C5846.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Digital images are often corrupted by contaminated display and information quality noise. Images can be corrupted at any stage during which they are acquired and transmitted through the media. Image denoising is a basic function designed to eliminate noise from naturally corrupted images. This work proposes a fixed-point discrete wavelet transform (DWT) architecture that uses a nonlinearly modified pixel-like weighted frame (PLWF) technique to denoise the high-throughput of adaptive white Gaussian white noise (AWGN) images. The linearized state to be based on the neighboring pixel unity is that the state model noise is used to improve the peak signal to the sound rate (PSNR). The proposed architecture is employed in two different stages – consistent and conditional sorting output selection unit. The detailed result of the proposed architecture shows the size and display quality of any state-of-the-art performance and some recently introduced work. For further evaluation of the denoising capability, the algorithm is compared to some state-of-the-art algorithms and experimental results on simulated sound images and captured images of low-light noise especially large image processes Low noise light picked up by the test results. The performance of the proposed method is compared to wavelet thresholds, bilateral filters, non-local averaging filters, and bilateral multi-resolution filters. The study found that the draft production plan is smaller than the wavelet threshold, the bilateral filter, and the non-local means of filtering and larger superior/similar to the method, visual quality, PSNR and image index noise bilateral multi-resolution filter quality.
Keywords: Discrete Wavelet Transform, Adaptive White Gaussian Noise, Pixel Likeness Weighted Frame, Peak Signal to Noise Ratio, Denoising
Scope of the Article: Computational Techniques in Civil Engineering