Abstract
The main objective of de-noising in images is to remove the unnecessary noise from the image while preserving the essential signal features as much as possible. Different methods are used for eliminating noises from digital images. The major role in de-noising is choosing a suitable method for the denoising process. Thus, various methods are suggested for image de-noising and all methods have its merits and demerits. This approach explores a Neural Network and Nature-Inspired Optimization Algorithm based denoising method implemented using Dual-Tree Complex wavelet Transform (DT-CWT). During this approach, initially the noisy image is converted into various sub-bands by applying DT-CWT. Then, to acquire noise-free wavelet coefficients, a trained Neural Network is applied on every sub-band. On these output wavelet coefficients Thresholding operation is performed with optimized threshold value by Genetic Algorithm. Now denoised image is generated through inverse DT-CWT. Relating to other existed methods this method gives improved results.
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Venkata Lavanya, P., Venkata Narasimhulu, C., Satya Prasad, K. (2022). Image Denoising Using an Artificial Neural Network and Genetic Optimization Algorithm Based Dual-Tree Complex Wavelet Transform. In: Reddy, V.S., Prasad, V.K., Wang, J., Reddy, K.T.V. (eds) Soft Computing and Signal Processing. Advances in Intelligent Systems and Computing, vol 1340. Springer, Singapore. https://doi.org/10.1007/978-981-16-1249-7_37
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DOI: https://doi.org/10.1007/978-981-16-1249-7_37
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