Online Reweighted Least Squares Algorithm for Sparse Recovery and Application to Short-Wave Infrared Imaging
This work addresses the need for progressive decoding and reduced computational/storage constraints in applications like imaging, though it is incremental as it builds on existing batch methods.
The paper tackles the problem of sparse signal recovery in an online setting by proposing a reweighted least squares algorithm that processes measurements sequentially, achieving up to 1 dB improvement in peak signal-to-noise ratio compared to total variation-based reconstruction in short-wave infrared imaging.
We address the problem of sparse recovery in an online setting, where random linear measurements of a sparse signal are revealed sequentially and the objective is to recover the underlying signal. We propose a reweighted least squares (RLS) algorithm to solve the problem of online sparse reconstruction, wherein a system of linear equations is solved using conjugate gradient with the arrival of every new measurement. The proposed online algorithm is useful in a setting where one seeks to design a progressive decoding strategy to reconstruct a sparse signal from linear measurements so that one does not have to wait until all measurements are acquired. Moreover, the proposed algorithm is also useful in applications where it is infeasible to process all the measurements using a batch algorithm, owing to computational and storage constraints. It is not needed a priori to collect a fixed number of measurements; rather one can keep collecting measurements until the quality of reconstruction is satisfactory and stop taking further measurements once the reconstruction is sufficiently accurate. We provide a proof-of-concept by comparing the performance of our algorithm with the RLS-based batch reconstruction strategy, known as iteratively reweighted least squares (IRLS), on natural images. Experiments on a recently proposed focal plane array-based imaging setup show up to 1 dB improvement in output peak signal-to-noise ratio as compared with the total variation-based reconstruction.