2 research outputs found
Measurement-Adaptive Sparse Image Sampling and Recovery
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
Forensic Discrimination between Traditional and Compressive Imaging Systems
Compressive sensing is a new technology for modern computational imaging
systems. In comparison to widespread conventional image sensing, the
compressive imaging paradigm requires specific forensic analysis techniques and
tools. In this regards, one of basic scenarios in image forensics is to
distinguish traditionally sensed images from sophisticated compressively sensed
ones. To do this, we first mathematically and systematically model the imaging
system based on compressive sensing technology. Afterwards, a simplified
version of the whole model is presented, which is appropriate for forensic
investigation applications. We estimate the nonlinear system of compressive
sensing with a linear model. Then, we model the imaging pipeline as an inverse
problem and demonstrate that different imagers have discriminative degradation
kernels. Hence, blur kernels of various imaging systems have utilized as
footprints for discriminating image acquisition sources. In order to accomplish
the identification cycle, we have utilized the state-of-the-art Convolutional
Neural Network (CNN) and Support Vector Machine (SVM) approaches to learn a
classification system from estimated blur kernels. Numerical experiments show
promising identification results. Simulation codes are available for research
and development purposes