eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2024-09-30 17 3 1875 1898 10.13005/bpj/2991 60786 article Swapna Katta 1 Prabhishek Singh 2 Deepak Garg 1 Manoj Diwakar 3 School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, India School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India Department of CSE, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India Department of CSE, Department of CSE, Graphic Era Hill University, Dehradun, India The presence of gaussian noise commonly weakens the diagnostic precision of low-dose CT imaging. A novel CT image denoising technique that integrates the non-subsampled shearlet transform (NSST) with Bayesian thresholding, and incorporates a modern method noise Deep Convolutional neural network (DCNN) based post-processing operation on denoised images to strengthen low-dose CT imaging quality. The hybrid method commences with NSST and Bayesian thresholding to mitigate the initial noise while preserving crucial image features, such as corners and edges. The novel aspect of the proposed approach is its successive application of a DnCNN on initial denoised image, which learns and removes residual noise patterns from denoised images, thereby enhancing fine detail preservation. This dual-phase strategy addresses both noise suppression and image-detail preservation. The proposed technique is evaluated through the use of metrics, such as PSNR, SNR, SSIM, ED, and UIQI. The results demonstrate that the hybrid approach outperforms standard denoising techniques in preserving image quality and fine details. https://biomedpharmajournal.org/vol17no3/a-hybrid-approach-for-ct-image-noise-reduction-combining-method-noise-cnn-and-shearlet-transform/ DnCNN LDCT imaging Gaussian noise Method Noise NSST