This letter introduces a physics-informed self-supervised framework for sonar image despeckling that reformulates despeckling as residual consistency in the homomorphic log domain. By constraining the log-ratio residual to obey multiplicative speckle statistics, the proposed method eliminates the need for clean supervision while preventing degenerate identity solutions. A variance-targeted statistical loss combined with edge-aware structural regularization and median-guided curriculum stabilization enables effective speckle suppression with preserved structural fidelity. This formulation along with a lightweight neural network achieves state-of-the-art performance across multiple real sonar datasets and demonstrates excellent cross-dataset robustness, while remaining suitable for real-time deployment.

Approximate computing is a rising technique aimed at developing arithmetic circuits that minimize power usage, resource consumption, and latency for error-tolerant applications, enabling faster and more efficient circuits suitable for resource-constrained environments. Square root computation is vital in hardware design for image and signal processing, but it’s resource-intensive and power-hungry. Optimizing it can boost power efficiency and performance. This work presents a hardware-efficient, fast and low-power Taylor series-based optimal unsigned square rooter (TSOSQR) designed for approximate square root computation of a 2n-bit unsigned integer using addition and shift operations. Compared to a precise restoring array-based square rooter architecture, the suggested square rooter design uses 79% fewer resources, operates 56% faster, and provides a 81% increase in power savings. These designs are implemented on an Artix-7 FPGA using Verilog-HDL, verified through Xilinx Vivado simulation, and additionally synthesized at the ASIC level using the Cadence Genus Compiler targeting a standard-cell 45nm CMOS technology node. Extensive simulations and a detailed comparison of the proposed design against state-of-the-art approaches show that the TSOSQR effectively balances accuracy and hardware efficiency, significantly reducing power consumption and latency. The paper demonstrates the superior performance of the proposed square rooter in Sobel edge detection, image contrast enhancement, and K-means clustering, with an IoT-enabled FPGA edge vision node design flow, highlighting its effectiveness and advantage over existing approximate designs.

This paper proposes, for the first time, a hybrid multiple access framework that integrates the principles of rate-splitting (RS) and sparse code multiple access (SCMA) in an SISO downlink scenario. The proposed scheme, termed RS-SCMA, unifies the powerful interference management capability of rate-splitting multiple access (RSMA) with the near-optimal multiuser detection of SCMA. A key feature of RS-SCMA is a tunable splitting factor α, which governs the allocation between the genericM-ary modulated common messages and SCMA-encoded private messages. This enables dynamic control over the fundamental trade-off between system sum-rate, bit error rate (BER), and the overloading factor. We develop novel transmitter and receiver architectures based on soft successive interference cancellation (SIC), incorporating message passing algorithm (MPA) detection and soft-symbol reconstruction. Furthermore, a unified analytical expression for the achievable sum-rate is derived as a function of the splitting factor α. The performance of the proposed RS-SCMA system is evaluated in terms of both BER and sum-rate. Simulation results confirm the superiority of RS-SCMA over conventional SCMA and multi-carrier RSMA, demonstrating its scalability and robustness even in the presence of channel estimation errors.

Image deblurring is a critical area of research, traditionally focused on scenarios where blurred images are formed by convolving a clean image with a blur kernel and adding white Gaussian noise. However, real-world images often suffer from non-uniform and non-stationary noise, which presents additional challenges that current methods struggle to address effectively. In this paper, we investigate the performance of the state-of-the-art techniques for handling non-uniform noise. Our simulation results show that these methods are not designed to handle the complexities associated with non-stationary noise. To overcome these limitations, we propose a novel technique that utilizes the estimated noise standard deviation map for better reconstruction. Through simulation results, we demonstrate that our proposed method significantly outperforms the state-of-the-art image deblurring methods in the presence of non-stationary noise.

In Compressive Sensing (CS) of sparse signals, standard$\ell_{1}$minimization can be effectively replaced with Weighted$\ell_{1}$-minimization$(\mathbf{W}\ell_{1})$if some information about the signal or its sparsity pattern is available. If no such information is available, Re-Weighted$\ell_{1}$-minimization$(\mathbf{ReW}\ell_{1})$can be deployed. ReW$\ell _{1}$solves a series of W$\ell_{1}$problems, and therefore, its computational complexity is high. An alternative to ReW$\ell_{1}$is the Greedy Pursuits Assisted Basis Pursuit (GPABP) which employs multiple Greedy Pursuits (GPs) to obtain signal information which in turn is used to run W$\ell_{1}$. Although GPABP is an effective fusion technique, it adapts a binary weighting strategy for running W$\ell_{1}$, which is very restrictive. In this article, we propose a gradual weighting strategy for W$\ell_{1}$, which handles the signal estimates resulting from multiple GPs more effectively compared to the binary weighting strategy of GPABP. The resulting algorithm is termed as Greedy Pursuits assisted Weighted$\ell_{1}$-minimization$(\mathbf{GP-W}\ell_{1})$. For GP-W$\ell_{1}$, we derive the theoretical upper bound on its reconstruction error. Through simulation results, we show that the proposed GP-W$\ell_{1}$outperforms ReW$\ell_{1}$and the state-of-the-art GPABP.

Distributed Compressive Sensing (DCS) is an extension of compressive sensing from single measurement vector problem to Multiple Measurement Vectors (MMV) problem. In DCS, several reconstruction algorithms have been proposed to reconstruct the joint-sparse signal ensemble. However, most of them are designed for signal ensemble sharing common support . Since the assumption of common sparsity pattern is very restrictive, we are more interested in signal ensemble containing both common and innovation components. With a goal of proposing an MMV-type algorithm that is robust to outliers (absence of common sparsity pattern), we propose Greedy Pursuits Assisted Basis Pursuit for Multiple Measurement Vectors (GPABP-MMV). It employs modified basis pursuit and MMV versions of multiple greedy pursuits. We also formulate the exact reconstruction conditions and the reconstruction error bound for GPABP-MMV. GPABP-MMV is suitable for a variety of applications including time-sequence reconstruction of video frames, reconstruction of ECG signals, etc.

Noise incursion is an inherent problem in dictionary training on noisy samples. Therefore, enforcing a structural constrain on the dictionary will be useful for a stable dictionary training. Recently, a sparse dictionary with predefined sparsity has been proposed as a structural constraint. However, a fixed sparsity can become too rigid to adapt to the training samples. In order to address this issue, this article proposes a better solution through sparse Sequential Generalization of K-means (SGK). The beauty of the sparse-SGK is that it does not enforce a predefined rigid structure on the dictionary. Instead, a flexible sparse structure automatically emerges out of the training samples depending on the amount of noise. In addition, a variation of sparse-SGK using an orthogonal base dictionary is proposed for a quicker training. The advantages of sparse-SGK are demonstrated via 3-D image denoising. The experimental results confirm that sparse-SGK has better denoising performance and it takes lesser training time.

In distributed compressive sensing, if one signal in a joint-sparse signal ensemble is known apriori, the remaining signals can be reconstructed using modified Compressive Sensing (CS) algorithms such as Modified Basis Pursuit (Mod-BP) which makes use of Partially Known Support (PKS). Though Mod-BP reconstructs the joint-sparse signals with high accuracy, it takes a huge amount of time to converge. This might not be desirable in some practical applications like CS reconstruction of video frames. Carrillo et al have illustrated the use of PKS in iterative greedy algorithms to improve the recovery performance at a much shorter time. However, PKS based iterative greedy algorithms are totally blind about the wrong atoms present in the PKS, which is likely for video frames. To overcome this, we propose Adaptive Backtracking Matching Pursuit (AdBMP) which makes effective use of the PKS to reconstruct the sparse signal. Experimental results show that AdBMP gives a better reconstruction accuracy compared to that of the existing PKS based iterative greedy algorithms.

Existing image denoising frameworks via sparse representation using learned dictionaries have an weakness that the dictionary, trained from noisy image, suffers from noise incursion. This paper analyzes this noise incursion, explicitly derives the noise component in the dictionary update step, and provides a simple remedy for a desired signal to noise ratio. The remedy is shown to perform better both in objective and subjective measures for lesser computation, and complements the framework of image denoising.

In Distributed Compressive Sensing (DCS), correlated sparse signals stand for an ensemble of signals characterized by presenting a sparse correlation. If one signal is known apriori, the remaining signals in the ensemble can be reconstructed using l1-minimization with far fewer measurements compared to separate CS reconstruction. Reconstruction of such correlated signals is possible via Modified-CS and Regularized-Modified-BP. However, these methods are greatly influenced by the support set of the known signal that includes locations irrelevant to the target signal. While recovering each signal, prior to Modified-CS or Regularized-Modified-BP, we propose an adaptation step to retain only the sparse locations significant to that signal. We call our proposed methods as Modified-Adaptive-BP and Regularized-Modified-Adaptive-BP. Theoretical guarantees and experimental results show that our proposed methods provide efficient recovery compared to that of the Modified-CS and its regularized version.

Recent dictionary training algorithms for sparse representation like K-SVD, MOD, and their variation are reminiscent of K-means clustering, and this letter investigates such algorithms from that viewpoint. It shows: though K-SVD is sequential like K-means, it fails to simplify to K-means by destroying the structure in the sparse coefficients. In contrast, MOD can be viewed as a parallel generalization of K-means, which simplifies to K-means without perturbing the sparse coefficients. Keeping memory usage in mind, we propose an alternative to MOD; a sequential generalization of K-means (SGK). While experiments suggest a comparable training performances across the algorithms, complexity analysis shows MOD and SGK to be faster under a dimensionality condition.