Measurement-Adaptive Sparse Image Sampling and Recovery

by   Ali Taimori, et al.

This paper presents an adaptive and intelligent sparse model for digital image sampling and recovery. In the proposed sampler, we adaptively determine the number of required samples for retrieving image based on space-frequency-gradient information content of image patches. By leveraging texture in space, sparsity locations in DCT domain, and directional decomposition of gradients, the sampler structure consists of a combination of uniform, random, and nonuniform sampling strategies. For reconstruction, we model the recovery problem as a two-state cellular automaton to iteratively restore image with scalable windows from generation to generation. We demonstrate the recovery algorithm quickly converges after a few generations for an image with arbitrary degree of texture. For a given number of measurements, extensive experiments on standard image-sets, infra-red, and mega-pixel range imaging devices show that the proposed measurement matrix considerably increases the overall recovery performance, or equivalently decreases the number of sampled pixels for a specific recovery quality compared to random sampling matrix and Gaussian linear combinations employed by the state-of-the-art compressive sensing methods. In practice, the proposed measurement-adaptive sampling/recovery framework includes various applications from intelligent compressive imaging-based acquisition devices to computer vision and graphics, and image processing technology. Simulation codes are available online for reproduction purposes.


page 6

page 7

page 8

page 9


Improved recovery guarantees and sampling strategies for TV minimization in compressive imaging

In this paper, we consider the use of Total Variation (TV) minimization ...

Single pixel imaging at high pixel resolutions

The usually reported pixel resolution of single pixel imaging (SPI) vari...

Adaptive Dictionary Sparse Signal Recovery Using Binary Measurements

One-bit compressive sensing is an extended version of compressed sensing...

A fast patch-dictionary method for whole image recovery

Various algorithms have been proposed for dictionary learning. Among tho...

MOSAIC: Masked Optimisation with Selective Attention for Image Reconstruction

Compressive sensing (CS) reconstructs images from sub-Nyquist measuremen...

A preconditioning approach for improved estimation of sparse polynomial chaos expansions

Compressive sampling has been widely used for sparse polynomial chaos (P...

Cellular Automata based adaptive resampling technique for the processing of remotely sensed imagery

Resampling techniques are being widely used at different stages of satel...

Please sign up or login with your details

Forgot password? Click here to reset