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A two-stage filter for high density salt and pepper denoising |
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| Authors: | Thanh Dang N. H. Hai Nguyen Hoang Prasath V. B. Surya Hieu Le Minh Tavares João Manuel R. S. |
| Affiliation: | 1.Department of Information Technology, School of Business Information Technology, University of Economics Ho Chi Minh city, Ho Chi Minh City, Vietnam ;2.Vietnam-Korea University of Information and Communication Technology – The University of Danang, Danang, Vietnam ;3.Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA ;4.Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA ;5.Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA ;6.Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA ;7.Department of Economics, University of Economics – The University of Danang, Danang, Vietnam ;8.Departamento de Engenharia Mecanica, Faculdade de Engenharia, Instituto de Ciência e Inova??o em Engenharia Mecanica e Engenharia Industrial, Universidade do Porto, Porto, Portugal ; |
| Abstract: | Image restoration is an important and interesting problem in the field of image processing because it improves the quality of input images, which facilitates postprocessing tasks. The salt-and-pepper noise has a simpler structure than other noises, such as Gaussian and Poisson noises, but is a very common type of noise caused by many electronic devices. In this article, we propose a two-stage filter to remove high-density salt-and-pepper noise on images. The range of application of the proposed denoising method goes from low-density to high-density corrupted images. In the experiments, we assessed the image quality after denoising using the peak signal-to-noise ratio and structural similarity metric. We also compared our method against other similar state-of-the-art denoising methods to prove its effectiveness for salt and pepper noise removal. From the findings, one can conclude that the proposed method can successfully remove super-high-density noise with noise level above 90%. |
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