Joint sparse representation (JSR) model has recently emerged as a powerful technique with wide variety of applications. In this paper, the JSR model is extended to error concealment (EC) application, being effective to recover the original image from its corrupted version. This model is based on jointly learning a dictionary pair and two mapping matrices that are trained offline from external training images. Given the trained dictionaries and mappings, the restoration is done by transferring the recovery problem into the sparse representation domain with respect to the trained dictionaries, which is further transformed into a common space using the respective mapping matrices. Then, the reconstructed image is obtained by back projection into the spatial domain. In order to improve the accuracy and stability of the proposed JSR-based EC algorithm and avoid unexpected artifacts, the local and non-local priors are seamlessly integrated into the JSR model. The non-local prior is based on the self-similarity within natural images and helps to find an accurate sparse representation by taking a weighted average of similar areas throughout the image. The local prior is based on learning the local structural regularity of the natural images and helps to regularize the sparse representation, exploiting the strong correlation in the small local areas within the image. Compared with the state-of-the-art EC algorithms, the results show that the proposed method has better reconstruction performance in terms of objective and subjective evaluations.
n this paper we present a new image sensor architecture for fast and accurate compressive sensing (CS) of natural images. Measurement matrices usually employed in compressive sensing CMOS image sensors (CS-CIS) are recursive pseudo-random binary matrices. We have proved that the restricted isometry property (RIP) of these matrices is limited by a low sparsity constant. The quality of these matrices is also affected by the non-idealities of pseudo-random numbers generators (PRNG). To overcome these limitations, we propose a hardware-friendly pseudo-random ternary measurement matrix generated on-chip by means of class III elementary cellular automata (ECA). These ECA present a chaotic behaviour that emulates random CS measurement matrices better than other PRNG. We have combined this new architecture with a block-based CS smoothed-projected Landweber (BCS-SPL) reconstruction algorithm. By means of single value decomposition (SVD) we have adapted this algorithm to perform fast and precise reconstruction while operating with binary and ternary matrices. Simulations are provided to qualify the approach
A cell-free massive multiple-input multiple-output
(MIMO) uplink is considered, where quantize-and-forward (QF)
refers to the case where both the channel estimates and the
received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU) whereas in combinequantize-
and-forward (CQF), the APs send the quantized version
of the combined signal to the CPU. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is exploited to convert the power allocation problem into a standard geometric programme (GP). We exploit the knowledge of the channel statistics to design the power elements. Employing largescale-fading (LSF) with a deep convolutional neural network (DCNN) enables us to determine a mapping from the LSF coefficients and the optimal power through solving the sum rate maximization problem using the quantized channel. Four possible power control schemes are studied, which we refer to as i) small-scale fading (SSF)-based QF; ii) LSF-based CQF; iii) LSF use-and-then-forget (UatF)-based QF; and iv) LSF deep
learning (DL)-based QF, according to where channel estimation is performed and exploited and how the optimization problem
is solved. Numerical results show that for the same fronthaul rate, the throughput significantly increases thanks to the mapping obtained using DCNN.
We consider a cell-free massive multiple-input
multiple-output (MIMO) system where the channel estimates and
the received signals are quantized at the access points (APs)
and forwarded to a central processing unit (CPU). Zero-forcing
technique is used at the CPU to detect the signals transmitted
from all users.. To solve the non-convex sum rate maximization
problem, a heuristic sub-optimal scheme is proposed to convert
the problem into a geometric programme (GP). Exploiting a deep
convolutional neural network (DCNN) allows us to determine
both a mapping from the large-scale fading (LSF) coefficients
and the optimal power by solving the optimization problem
using the quantized channel. Depending on how the optimization
problem is solved, different power control schemes are investigated;
i) small-scale fading (SSF)-based power control; ii) LSF
use-and-then-forget (UatF)-based power control; and iii) LSF
deep learning (DL)-based power control. The SSF-based power
control scheme needs to be solved for each coherence interval
of the SSF, which is practically impossible in real time systems.
Numerical results reveal that the proposed LSF-DL-based scheme
significantly increases the performance compared to the practical
and well-known LSF-UatF-based power control, thanks to the
mapping obtained using DCNN.