An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network

ABSTRACT

Image denoising is one of the significant approaches for extracting valuable information in the required images without any errors. During the process of image transmission in the wireless medium, a wide variety of noise is presented to affect the image quality. For efficient analysis, an effective denoising approach is needed to enhance the quality of the images. The main scope of this research paper is to correct errors and remove the effects of channel degradation. A corrupted image denoising approach is developed in wireless channels to eliminate the bugs. The required images are gathered from wireless channels at the receiver end. Initially, the collected images are decomposed into several regions using Adaptive Lifting Wavelet Transform (ALWT) and then the “Symmetric Convolution-based Residual Attention Network (SC-RAN)” is employed, where the residual images are obtained by separating the clean image from the noisy images. The parameters present are optimized using Hybrid Energy Golden Tortoise Beetle Optimizer (HEGTBO) to maximize efficiency. The image denoising is performed over the obtained residual images and noisy images to get the final denoised images. The numerical findings of the developed model attain 31.69% regarding PSNR metrics. Thus, the analysis of the developed model shows significant improvement.

KEYWORDS:

• Wireless channel corrupted image denoising
• decomposition
• adaptive lifting wavelet transform
• parameter optimization
• symmetric convolution-based residual attention network
• hybrid energy golden tortoise beetle optimizer

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Funding

The author(s) reported there is no funding associated with the work featured in this article.