A Denoising Algorithm for CT Image Using Low-rank Sparse Coding

We propose a denoising method of CT image based on low-rank sparse coding. The proposed method constructs an adaptive dictionary of image patches and estimates the sparse coding regularization parameters using the Bayesian interpretation. A low-rank approximation approach is used to simultaneously construct the dictionary and achieve sparse representation through clustering similar image patches. A variable-splitting scheme and a quadratic optimization are used to reconstruct CT image based on achieved sparse coefficients. We tested this denoising technology using phantom, brain and abdominal CT images. The experimental results showed that the proposed method delivers state-of-art denoising performance, both in terms of objective criteria and visual quality.