Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction

The sparse recovery methods utilize the ℓ
_p
-norm-based regularization in the estimation problem with 0 ≤ p ≤ 1. These methods have a better utility when the number of independent 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 utilize the ℓ
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.