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Performance comparison of deep convolution neural network and median filter in terms of denoising and detail preserving

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Abstract

Denoising is an important preprocessing step that aids many computer-aided diagnosis systems, computer graphics, and computer vision applications; however, the tradeoff between reducing noise and preserving details in images is not trivial, especially in medical images. Therefore, this paperwork proposes two weighted factors besides using two popular factors to investigate deeply the performance of the deep convolutional neural network and median filter in reducing different kinds of noise and mixed noise with various noise percentages. After the intensive analysis, the assessment results show that the convolutional Neural network outperformed the median in terms of denoising and detail preserving in reducing several noises, especially with images that are highly corrupted by specular noise. The efficiency rank in preserving details during reducing low and high ratios of different types of noises by each method was also rated. The proposed solutions can be used for automatic denoising of the mentioned noises with controlling the detail preserving using our proposed weighted factors.

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  1. Department of Computer Engineering, College of Engineering, Mustansiriyah University, Baghdad, Iraq

    Nidhal Azawi

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  1. Nidhal Azawi
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Azawi, N. Performance comparison of deep convolution neural network and median filter in terms of denoising and detail preserving. Multimed Tools Appl 82, 45733–45745 (2023). https://doi.org/10.1007/s11042-023-16336-3

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  • DOI: https://doi.org/10.1007/s11042-023-16336-3

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