De-noising of salt and pepper noise using deep learning-based alpha-guided grey wolf optimization

The real images are undermined by salt and pepper noise due to uproarious sensors or communication errors. In this paper, a hybrid SAR image noise reduction using the adaptive pulse-coupled neural network (APCNN) which is optimized by an alpha-guided grey wolf optimizer (AgGWO) in the shearlet transform domain has been proposed. The shearlet transform is utilized to decompose the input SAR image. After the completion of AgGWO optimization, PCNN filtering strategy has been utilized to supplant the noisy pixels into related information pixel components, from which a restoration of the noise reduced images can be obtained. This proposed methodology efficiently resolves the difficulties that emerge from the basic PCNN parameter determination problem. In this methodology, the noisy pixels are secluded well from the image and original noiseless pixels are reestablished well, which can prompt better conservation of edges of an image. This proposed APCNN-AgGWO method has also been compared with other existing noise reduction methods and it yields superior de-noising impact, in terms of structural similarity index measure (SSIM) of 98.46%, peak signal-to-noise ratio (PSNR) of 39.49%, and standard deviation (STD) of 38.64%. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a hybrid noise reduction method for synthetic aperture radar (SAR) images using the adaptive pulse-coupled neural network (APCNN) and grey wolf optimizer (GWO) in the shearlet transform domain. The proposed method effectively addresses the parameter determination problem in noise filtering and improves the preservation of image edges.