Single-pixel compressive imaging based on random DoG filtering

As its name implies, compressive sensing aims to bring compression during sampling. However, the deployment of this technique depends on recovering a high fidelity image through a low number of measurements with a simple hardware and fast software. To this end, we introduce an encoding scheme that by filtering the scene acquires information about the image structure. To prepare a set of proposed encoding patterns, at the first step, a filter bank containing a number of Difference of Gaussian (DoG) kernels with different scales is prepared. Then, by randomly selecting the filters from the bank and under-sampling the scene with them at random points, each encoding pattern is constructed. The idea is inspired by the Human Visual System (HVS) that uses a set of size-tuned DoG kernels at each point in the field-of-view. These encoding patterns, which make a set of linearly independent vectors, form the rows of a structured measurement matrix. This matrix allows making relatively well-conditioned dictionaries by different sparsifying bases. The effectiveness of this method is confirmed by simulations and analyses. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

Compressive sensing aims to achieve high fidelity image recovery with a low number of measurements using an encoding scheme based on filtering the scene. The structured measurement matrix formed by the encoding patterns, which are linearly independent vectors, allows relatively well-conditioned dictionaries to be created using different sparsifying bases.