MOTF: Multi-objective Optimal Trilateral Filtering based partial moving frame algorithm for image denoising
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MOTF: Multi-objective Optimal Trilateral Filtering based partial moving frame algorithm for image denoising Rejeesh M R 1
& Thejaswini P
2
Received: 10 July 2019 / Revised: 4 June 2020 / Accepted: 24 June 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
In this paper, a novel denoising approach based on optimal trilateral filtering using Grey Wolf Optimization (GWO) is proposed. At first, a database of noisy images are generated by adding Gaussian noise, Salt & Pepper noise and Random noise to the captured image. The filtering of noisy images are performed by Block-matching and 3D filtering (BM3D) algorithm over the components of image obtained through the moving frame approach. Then, using optimal trilateral filtering, the denoised images are reconstructed. Therefore, by using a two-level filtering approach such as Moving frame-based Block-matching and 3D filtering (BM3D) and Optimal trilateral filtering the noisy images are decomposed. The proposed optimal trilateral filter employs Grey Wolf Optimization algorithm for selecting the parameters optimally to improve the efficiency of filtering method which also reduces the time required for manual computation. The performance of the proposed image denoising algorithm is analyzed using multiple datasets and the analysis of results were done in contrast with existing conventional approaches. The results validated that the optimal trilateral filtering approach outperforms other conventional methods in terms of Mean-Square Error (MSE) and the Peak Signal-to-Noise Ratio (PSNR). Keywords Image denoising . Moving frame . Optimal trilateral filtering . Grey Wolf Optimization . PSNR . MSE . Block-matching and 3D filtering (BM3D)
*
Rejeesh M R [email protected] Thejaswini P [email protected]
1
Anna University, Nagercoil, Tamil Nadu 600025, India
2
Department of ECE, JSS Academy of Technical Education Bangalore, Bengaluru, Karnataka 560060, India
Multimedia Tools and Applications
1 Introduction Image denoising is an important process for removing noise from medical as well as natural images. Image denoising area is very broad to review and research for various applications like computer graphics and vision [13, 14]. The main aim is to exterminate noise from tampered digital and medical images and improve the image quality [24]. The objective of image denoising is to exterminate noise and preserve vital features of the image [19]. In past decades, the denoising approaches were classified in to spatial and transform domain approaches [28]. The transform-based method is used to compute the wavelet coefficients of images and to reconstruct the images by using the inverse transform [34]. The local and non-local domains are the two stages of spatial and transform domain approaches. The local methods use local neighborhood for spatial redundancy and most of the transform-based strategy for denoising uses local process [17]. Researchers use various linear and non–linear filter-based strategies such as Wiener filter, Median filt
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