Abstract: We present a new algorithm for chromotomographic image restoration. The main stage of the algorithm employs the iterative method of projections onto convex sets, utilizing a new constraint operator. The constraint takes advantage of hyperspectral data redundancy and information compacting ability of singular value decomposition to reduce noise and artifacts. Results of experiments on both in-house and AVIRIS data demonstrate that the algorithm converges rapidly and delivers high image fidelity. !23
展开▼
