Sparse recovery methods hold promise for diffuse optical tomographic image reconstruction

Abstract—The sparse recovery methods utilize the `p-norm based regularization in the estimation problem with 0 ≤ p ≤ 1. These methods have a better utility when the number of indepen-dent measurements are limited in nature, which is a typical case for diffuse optical tomographic image reconstruction problem. These sparse recovery methods, along with an approximation to utilise the `0-norm, have been deployed for the reconstruction of diffuse optical images. Their performance was compared systematically using both numerical and gelatin phantom cases to show that these methods hold promise in improving the reconstructed image quality. Index Terms—near infrared imaging, diffuse optical tomogra-phy, image reconstruction, sparse recovery methods. I