Measurement-Adaptive Sparse Image Sampling and Recovery

TL;DR

This paper introduces an adaptive sparse sampling method for images that intelligently determines sample locations based on image content, leading to improved recovery performance and reduced sampling requirements in various imaging applications.

Contribution

It proposes a novel adaptive sampling scheme combined with a cellular automaton-based recovery algorithm, enhancing image reconstruction efficiency over traditional methods.

Findings

Significantly improves recovery performance with fewer samples.

Converges quickly within a few generations during reconstruction.

Outperforms state-of-the-art compressive sensing techniques.

Abstract

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.