Volume 7 | Issue -2
Volume 7 | Issue -2
Volume 7 | Issue -2
Volume 7 | Issue -2
Volume 7 | Issue -2
A medical imaging technique used for illness diagnosis and screening is low dose X-ray imaging. Unfortunately, mechanical noise makes it difficult to analyze such images. Despite significant advancements, some deep learning-based denoising methods still perform poorly on actual X-ray pictures. Due to the complexity of the X-ray image's actual noise. In order to replicate the true X-ray image, we create a noise model in this study based on the basic principles of X-ray imaging. Our proposal for improving low-dose X-ray images is to use a Blind Denoising Convolutional-Neural-Network (BDCNN). The experimental findings demonstrate that BDCNN outperforms the other denoising techniques in terms of denoising performance.