Low-dose computed tomography image denoising based on joint wavelet and sparse representation

One of the most challenging issues in low dose computed tomography (CT) imaging is image denoising and signal enhancement. Sparse representational methods have shown initial promise for these applications. In this thesis we present a wavelet based sparse representation denoising technique utilizing dictionary learning and clustering. By using wavelets we extract the most suitable features in the images to obtain accurate dictionary atoms for the denoising algorithm. To achieve improved results we also lower the number of clusters which reduces computational complexity. In addition, a single image noise level estimation is developed to update the cluster centers in higher PSNRs. A new image enhancement technique is developed for low-dose CT images to improve the quality of image for diagnostic purpose and reduce the blurring artifacts. The accuracy along with the computational efficiency of the proposed algorithm are then compared with recent approaches and clearly demonstrate the improvement of the proposed algorithm proposed in this thesis.