Deep Learning-Based CT Image Denoising Using Internal Residual Learning with DnCNN
Keywords:
image denoising, DnCNN, Internal Residual Learning, Gaussian filter, Non-Local medianAbstract
Image denoising is a fundamental task in image processing that aims to enhance the visual quality of images by removing unwanted noise introduced during acquisition, transmission, or storage. Noise can degrade visual content and affect image analysis tasks such as edge detection, object recognition, and feature extraction. The main challenge lies in balancing noise reduction with detail preservation. Traditional algorithms such as Gaussian and median filtering often blur important features of the images. This paper presents a deep learning scheme for denoising CT images, based on Internal Residual Learning with Deep Convolutional Neural Network (DnCNN). The model was used to suppress different levels of noise varying from 0.05 up to 0.4. The proposed model yielded very promising results compared to the classic Gaussian filter, Non-Local median (NLM), and standard DnCNN approaches.
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