IEEE Trans Image Process - Binary compressed imaging.

Tópicos

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Resumo

Compressed sensing can substantially reduce the number of samples required for conventional signal acquisition at the expense of an additional reconstruction procedure. It also provides robust reconstruction when using quantized measurements, including in the one-bit setting. In this paper, our goal is to design a framework for binary compressed sensing that is adapted to images. Accordingly, we propose an acquisition and reconstruction approach that complies with the high dimensionality of image data and that provides reconstructions of satisfactory visual quality. Our forward model describes data acquisition and follows physical principles. It entails a series of random convolutions performed optically followed by sampling and binary thresholding. The binary samples that are obtained can be either measured or ignored according to predefined functions. Based on these measurements, we then express our reconstruction problem as the minimization of a compound convex cost that enforces the consistency of the solution with the available binary data under total-variation regularization. Finally, we derive an efficient reconstruction algorithm relying on convex-optimization principles. We conduct several experiments on standard images and demonstrate the practical interest of our approach.

Resumo Limpo

compress sens can substanti reduc number sampl requir convent signal acquisit expens addit reconstruct procedur also provid robust reconstruct use quantiz measur includ onebit set paper goal design framework binari compress sens adapt imag accord propos acquisit reconstruct approach compli high dimension imag data provid reconstruct satisfactori visual qualiti forward model describ data acquisit follow physic principl entail seri random convolut perform optic follow sampl binari threshold binari sampl obtain can either measur ignor accord predefin function base measur express reconstruct problem minim compound convex cost enforc consist solut avail binari data totalvari regular final deriv effici reconstruct algorithm reli convexoptim principl conduct sever experi standard imag demonstr practic interest approach

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