Block Compressed Sensing of Natural Images

TLDR

Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. The paper proposes and studies block compressed sensing for natural images, acquiring data block‑by‑block with a single operator. Reconstruction uses a hybrid algorithm combining Wiener filtering, convex set projection, and hard thresholding in the transform domain. Numerical experiments show that the block CS scheme, though simpler and cheaper, captures complex natural image geometry and outperforms existing methods.

Abstract

Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. In this paper, we propose and study block compressed sensing for natural images, where image acquisition is conducted in a block-by-block manner through the same operator. While simpler and more efficient than other CS techniques, the proposed scheme can sufficiently capture the complicated geometric structures of natural images. Our image reconstruction algorithm involves both linear and nonlinear operations such as wiener filtering, projection onto the convex set and hard thresholding in the transform domain. Several numerical experiments demonstrate that the proposed block CS compares favorably with existing schemes at a much lower implementation cost.