Dictionary and Image Recovery from Incomplete and Random Measurements
This work solves the problem of blind compressed sensing for image processing by enabling accurate dictionary estimation from limited data, which is incremental as it builds on existing dictionary learning but removes reliance on structural assumptions.
The paper addresses dictionary learning and image recovery from incomplete random measurements, showing that a modified dictionary learning algorithm can accurately estimate the ideal dictionary for large-scale images without relying on structural constraints, with simulation results confirming it outperforms non-adaptive sparse recovery methods.
This paper tackles algorithmic and theoretical aspects of dictionary learning from incomplete and random block-wise image measurements and the performance of the adaptive dictionary for sparse image recovery. This problem is related to blind compressed sensing in which the sparsifying dictionary or basis is viewed as an unknown variable and subject to estimation during sparse recovery. However, unlike existing guarantees for a successful blind compressed sensing, our results do not rely on additional structural constraints on the learned dictionary or the measured signal. In particular, we rely on the spatial diversity of compressive measurements to guarantee that the solution is unique with a high probability. Moreover, our distinguishing goal is to measure and reduce the estimation error with respect to the ideal dictionary that is based on the complete image. Using recent results from random matrix theory, we show that applying a slightly modified dictionary learning algorithm over compressive measurements results in accurate estimation of the ideal dictionary for large-scale images. Empirically, we experiment with both space-invariant and space-varying sensing matrices and demonstrate the critical role of spatial diversity in measurements. Simulation results confirm that the presented algorithm outperforms the typical non-adaptive sparse recovery based on offline-learned universal dictionaries.