Research Article
Image Reconstruction in Compressive Sensing Using Biorthogonal 5.5 (bior5.5) and Lifting Wavelet Transforms with SP, CoSaMP, and ALISTA Algorithms
Issue:
Volume 12, Issue 2, December 2025
Pages:
16-29
Received:
20 October 2025
Accepted:
3 November 2025
Published:
19 December 2025
DOI:
10.11648/j.cssp.20251202.11
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Abstract: This paper proposes an efficient image reconstruction for compressive sensing (CS) that combines the Lifting Wavelet Transform (LWT) using Biorthogonal 5.5 (bior5.5) wavelets with three reconstruction algorithms: Subspace Pursuit (SP), Compressive Sampling Matching Pursuit (CoSaMP), and the Analytic Learned Iterative Shrinkage Thresholding Algorithm (ALISTA). Unlike the conventional Discrete Wavelet Transform (DWT) which relies on computationally intensive convolution operations the LWT provides a faster sparse representation while preserving the sparsity crucial for CS. The proposed approach leverages a key insight: among the four subbands produced by the LWT namely the approximation (CA) and the detail coefficients (LH, HL, HH) only the latter three are inherently sparse. Therefore, compressive sensing is applied exclusively to these detail subbands, while the CA subband is left uncompressed to retain essential low-frequency information. Experiments were conducted on both a natural test image (Lena) and a medical MRI scan, across image resolutions ranging from 200×200 to 512×512 pixels and sampling rates from 10% to 80%. Performance was assessed using the Structural Similarity Index (SSIM) and reconstruction time. Results consistently demonstrate that ALISTA significantly outperforms SP and CoSaMP in both reconstruction fidelity and computational efficiency. At an 80% sampling rate, ALISTA achieves SSIM values of 0.99409 for Lena and 0.9775 for the MRI image, compared to approximately 0.96 and 0.95644, respectively, for the other two methods. Furthermore, ALISTA maintains remarkably low reconstruction times under 4 seconds even for 512×512-pixel images. These findings confirm that the ALISTA + LWT/bior5.5 combination offers the best trade-off between image quality and speed, exhibiting robustness across different image types and scales.
Abstract: This paper proposes an efficient image reconstruction for compressive sensing (CS) that combines the Lifting Wavelet Transform (LWT) using Biorthogonal 5.5 (bior5.5) wavelets with three reconstruction algorithms: Subspace Pursuit (SP), Compressive Sampling Matching Pursuit (CoSaMP), and the Analytic Learned Iterative Shrinkage Thresholding Algorith...
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Research Article
Design and Implementation of a Low-Cost Multi-Waveform Generator Using Arduino Mega and AD9833 with LCD-Based Interactive Control
Shrugal Agarwal,
Priyam Parikh*
Issue:
Volume 12, Issue 2, December 2025
Pages:
30-46
Received:
15 October 2025
Accepted:
27 October 2025
Published:
24 December 2025
DOI:
10.11648/j.cssp.20251202.12
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Abstract: Signal generators are essential instruments for testing, measurement, and embedded system validation. Commercial function generators, however, are often expensive and non-customizable for educational or prototyping environments. This study presents the design and realization of a low-cost, microcontroller-based multi-waveform generator capable of producing sine, triangular, sawtooth, and square waveforms with adjustable frequency, phase, and duty cycle. The system integrates an Arduino Mega 2560 controller with an AD9833 Direct Digital Synthesis (DDS) module for high-precision sine and triangular outputs, while hardware-timed PWM channels generate sawtooth and square waveforms. Three potentiometers provide real-time user control of frequency (50 Hz-1 kHz), phase (0°-360°), and duty ratio (0-100%), and a 16×2 I²C LCD displays the selected waveform parameters. Experimental characterization demonstrates frequency accuracy of ±0.05% and phase error within ±2° for AD9833-based signals, and total harmonic distortion (THD) below 0.8% for sine output up to 1 kHz. PWM-derived waveforms exhibit amplitude linearity of 96-98% and negligible drift across 8 h continuous operation. Compared with conventional analog Wien-bridge or XR2206-based function generators, the proposed system offers higher frequency stability, lower power consumption (≈310 mW), and greater flexibility for digital control at less than 15 USD total cost. The developed prototype successfully reproduces clean, noise-free waveforms observable on an oscilloscope and matches reference laboratory generators with an RMS amplitude deviation under 0.03 V (5 V scale). The compact and modular design enables rapid educational deployment and portable instrumentation. Future enhancements may include amplitude modulation through DAC expansion, frequency sweep automation, and PC-linked waveform visualization. The proposed design thus bridges the gap between low-cost educational tools and professional waveform generation, demonstrating the potential of open-source microcontroller architectures for accurate, user-interactive signal synthesis.
Abstract: Signal generators are essential instruments for testing, measurement, and embedded system validation. Commercial function generators, however, are often expensive and non-customizable for educational or prototyping environments. This study presents the design and realization of a low-cost, microcontroller-based multi-waveform generator capable of p...
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