Filtering Methods for Biomedical Image Denoising

Published: Oct. 18, 2023

In this paper, the filtering method of biomedical image denoising is described comprehensively. Firstly, it introduces denoising, describes relationship between and medical care, methods, methods challenges encountered by current other application fields methods. background introduced. Biomedical a challenge. Different imaging modes have different noise characteristics, levels can vary greatly depending on specific application. Secondly, that plays an important role in directly affects patient's diagnosis, treatment plan overall quality care service. Then introduced detail, describing core concepts related features linear filtering, nonlinear frequency domain then focusing adaptive method, conditions use, common algorithms advantages method. filter are introduced, Gaussian filter, median total variation Wiener respectively. Then, described, such as accurate selection filters, balance reduction detail preservation Finally, mentioned, audio processing, speech recognition so on. summary, paper comprehensively expounds images,

Language: Английский

Image Noise Reduction with Auto-encoders using TensorFlow

International Journal of Advanced Research in Science Communication and Technology, Journal Year: 2024, Volume and Issue: unknown, P. 86 - 91

Published: June 28, 2024

Image noise reduction is a fundamental task in image processing with applications an assortment of fields, including medical imaging, satellite imaging and photography. In this project, we propose innovative method for denoising utilizing autoencoders, particular kind neural network particularly suited learning efficient representations data. We implement our solution using TensorFlow, popular deep framework, leveraging its flexibility performance capabilities. Autoencoders consist two encoders decoder, where the encoder maps input data into latent space lower dimensions representation, decoder restores initial from representation. By training autoencoder on pairs noisy clean images, it learns to capture underlying structure while filtering out noise. Furthermore, explore extensions enhancements basic model, incorporating adversarial techniques like GANs, or generative networks further enhance performance. also discuss potential future directions research autoencoders. summary, work presents comprehensive framework autoencoders implemented offering promising results insights addressing critical problem processing.

Language: Английский