Details

Autor(en) / Beteiligte

• Saini, Ashish
• Gill, Nasib Singh
• Gulia, Preeti

Titel

Gray scale image denoising technique using regression based residual learning

Ist Teil von

• Multimedia tools and applications, 2024, Vol.83 (2), p.3547-3566

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• In recent years the application of digital images has increased in a rapid manner, but due to noise the limited applications are currently openly adopting these applications. The noise can degrade the image quality and the application’s quality of service too. However, in literature, there are a number of different kinds of noise available and to rectify different types of noise different image filtering techniques are also available. But most of the techniques are computationally expensive or less effective, less efficient, and not able to preserve the image features for higher levels of noise. Therefore, in this paper, we introduced an optimization technique for impulse noise removal and measured its effect on the different levels of noise in the image. The proposed filter detects and removes impulse noise from digital grey-scale images. Thus the algorithm first classifies the image pixels in terms of noisy and non-noisy pixels. Here the classification of pixels has been carried out using the regression analysis of the image vector. After locating the corrupted pixel the mean of self and neighbor pixels which are non-noisy (except pixel values 0 and 255) has been used to replace the noisy pixel. However, this technique is not completely removing the noise in a single step thus we eliminate the noise in an iterative manner. Additionally to deal with the blurring effect and to preserve the image edges we employ L0 smoothing. Finally, in the last step, we utilize the median filter for constructing the final output image. The simulation of the proposed algorithm has been carried out with MATLAB and with the help of a publically available dataset. The experiments have been carried out and performance is measured in terms of the visual quality histogram and PSNR (pick signal to noise ratio). The comparison with the relevant techniques demonstrates the effective denoising consequences of the proposed technique.