Lensless Compressive Imaging

Abstract

We develop a lensless compressive imaging architecture, which consists of an aperture assembly and a single sensor, without using any lens. An anytime algorithm is proposed to reconstruct images from the compressive measurements; the algorithm produces a sequence of solutions that monotonically converge to the true signal (thus, anytime). The algorithm is developed based on the sparsity of local overlapping patches (in the transformation domain) and state-of-the-art results have been obtained. Experiments on real data demonstrate that encouraging results are obtained by measuring about 10% (of the image pixels) compressive measurements. The reconstruction results of the proposed algorithm are compared with the JPEG compression (based on file sizes) and the reconstructed image quality is close to the JPEG compression, in particular at a high compression rate.

37 pages, 10 figures. Submitted to SIAM Journal on Imaging Science

References in corpus (3)

• Compressive Hyperspectral Imaging with Side Information
• Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization
• A new compressive video sensing framework for mobile broadcast

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• Convergence of the Generalized Alternating Projection Algorithm for Compressive Sensing