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Wireless channel corrupted image denoising using residual learning of adaptive wavelet with dilated deep convolutional neural network

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Abstract

In digital image transmission, channel coding is essential for data integrity and accurate reception during digital communication. While minimizing the consequences of the channel, typical communication methods neglect the received context and content-related data. Many significant restrictions are applied to image transmission in digital communication. Due to the limited qualities of transferred data, image quality suffers across over wireless networks. Moreover, the transmission of digital images is a complicated task based on their shape, size, and bandwidth. Hence, it lacks flexibility and practicality in the real-world environment. Different image-denoising techniques are employed to decrease the noisy image channel effects. Thus, this research aims to analyze the images gathered during wireless channel communication to precisely eliminate the bugs and the impact of channel degradation. The image denoising is carried out during the wireless channel communication process at the receiver end. A novel deep Residual Learning of Adaptive Wavelet with Dilated Deep Convolutional Neural Network (RL-AWDDCNN) method is used to denoise the image more effectively. Hence, the residual images are generated based on the heuristic concept, where an improved flow direction algorithm (IFDA) is developed for optimizing the wavelet parameters with Dilated Deep Convolutional Neural Network. Throughout the result analysis, the designed method scored a 3037.233% peak signal-to-noise ratio (PSNR) rate and an 80.18675% structural similarity index measure (SSIM) rate. Thus, the developed denoising model’s performance and the experimental results are analyzed and compared with various existing methods concerning the standard image quality measures.

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Data availability

The data underlying this article are available in CBSD68-dataset, at https://github.com/clausmichele/CBSD68-dataset: access date: 2022-12-13, Smartphone Image Denoising Dataset, at https://www.kaggle.com/datasets/rajat95gupta/smartphone-image-denoising-dataset: access date: 2022-12-13 and PolyU-Real-World-Noisy-Images-Dataset, at “https://github.com/csjunxu/PolyU-Real-World-Noisy-Images-Dataset: access date: 2022–12-13.”

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Authors and Affiliations

  1. Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India

    M. Sreedhar

  2. ECE Department, JNTUA College of Engineering, Kalikiri, Constituent College of Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India

    K. Aparna

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  1. M. Sreedhar
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All authors have made substantial contributions to the conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Sreedhar, M., Aparna, K. Wireless channel corrupted image denoising using residual learning of adaptive wavelet with dilated deep convolutional neural network. SIViP 18, 2309–2321 (2024). https://doi.org/10.1007/s11760-023-02907-2

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