Improved Image Denoising with the Integrated Model of Gaussian filter and Neighshrink SURE
Mukund N Naragund1, Basavaraj N Jagadale2, Priya B S3, Panchaxri4, Vijayalaxmi Hegde5
1Mukund N Naragund*, Department of Physics and Electronics, CHRIST (Deemed to be University), Bengaluru, India.
2Basavaraj N Jagadale, Department of PG studies and research in Electronics, Kuvempu University, Shimoga, India.
3Priya B S, Department of Electronics, Kuvempu University, Shimoga, India.
4Panchaxri, Department of Electronics, SSA Govt. First grade College, Ballari, India.
5Vijayalaxmi Hegde, Department of Electronics, MESMM Arts and Science College, Sirsi, India.
Manuscript received on August 03, 2019. | Revised Manuscript received on August 28, 2019. | Manuscript published on August 30, 2019. | PP: 3010-3015 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9020088619/2019©BEIESP | DOI: 10.35940/ijeat.F9020.088619
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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: Image denoising, being an important preprocessing stage in image processing, minimizes the noise interfering with the information content of the image. The denoising problems are addressed by various techniques starting from the Fourier transforms to wavelets. Because of the localized time frequency features and advantages of multi resolution capabilities, the wavelets have been extensively used in the denoising process. The development of algorithms for the wavelet thresholding or shrinkage strategies along with different filters have resulted in the betterment of image quality after the denoising. Even though the image denoising algorithm based on a combination of Gaussian and Bilateral filters, shows good performance but lacks in consistency with respect to the noise levels and also the type of images used. This paper discusses the advantages of Neigh Shrink SURE rule in developing an effective thresholding strategy, thereby proposing a improved denoising method incorporating the Neigh Shrink SURE rule along with combination of Gaussian filter model. The methodology employs the use of subband thresholding derived from the Neigh Shrink SURE rule. The outcome of the proposed method exhibits a comparatively improved performance in Peak Signal to Ratio (PSNR) and Image Quality Index (IQI) values of the test images.
Keywords: Image denoising; Gaussian filter; Wavelet thresholding; Neigh Shrink SURE.