Fast low dose Computed Tomography image Super Resolution Reconstruction via Sparse coding and Random Forests
X ray radiation is harmful to human health. Therefore, how to obtain a better quality reconstructed image with low dose scan is a major challenge in the field of computed tomography (CT) This paper proposes a fast low dose CT super resolution method based on sparse coding and random forests. By using high resolution training images and low resolution training images to obtain high resolution dictionaries and using back projection to ensure global consistency, and finally using sparse coding to extract and fuse useful information in low dose CT images, random forests complete classification. The experimental results show that compared with the dictionary learning method and the traditional interpolation method, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) of CT images obtained by this method are the highest, and the reconstructed images are the most robust and have a fast running speed and training speed.
2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)