Progression approach for image denoising

• Published in: Telkomnika 2019-12, Vol.17 (6), p.2948-2958
• Main Authors: Charmouti, Bilal, Junoh, Ahmad Kadri, Mashor, Mohd Yusoff, Ghazali, Najah, Wahab, Mahyun Ab, Wan Muhamad, Wan Zuki Azman, Yahya, Zainab, Beroual, Abdesselam
• Format: Article
• Language: English
• Subjects: Algorithms; Astronomy; Digital imaging; Fuzzy sets; Image degradation; Image filters; Image processing; Image quality; Image restoration; Image transmission; Literature reviews; Noise; Noise measurement; Noise reduction; Pixels; Random noise; Researchers; Signal processing
• ISSN: 1693-6930; 2302-9293
• DOI: 10.12928/telkomnika.v17i6.12408

Removing noise from the image by retaining the details and features of this treated image remains a standing challenge for the researchers in this field. [...]this study is carried out to propose and implement a new denoising technique for removing impulse noise from the digital image, using a new way. Keywords: arithmetic progression, denoising technique, image, image processing, impulse noise, noise (ProQuest: ... denotes formulae omitted.) 1.Introduction Image is considered as a powerful platform to carry and to transmit information between people, where it is very important in a lot of fields such as biology, astronomy, industrial, medical and surveillance [1]. [...]it attracts the attention of a lot of researchers in restoring the unknown original image from the degraded image caused by any factors that may degrade or reduce the image quality (e.g. blur). [...]Denoising image is a critical and primary phase in the image processing (preprocessing phase), aiming to remove or reduce the noise from the noisy image by preserving the image features, using the various techniques (filters). Restored image (output): measurement of noise (SNR, MSE, PSNR, ect.), visual quality (blur, artifacts, information loss and etc). in brief, this section provides the necessary information restricted in the primary phase of image processing (image pre-processing), which is called Image denoising. 2.Literature Review 2.1.Impulse Noise In case of the grayscale image, the impulse noise may be represented by random values (RV) of pixels (value between 0 to 255) in the corrupted image, or by fixed values (FV) also called "salt & pepper" noise produced by random partial distribution of white pixels (value 255) and black pixels (value 0) into the image [7], as shown in Figure 1, unlike gaussian noise with the entire distribution (all image pixels) [8].