Research Article
Image Reconstruction in Compressive Sensing Using the Level 3 Symlet 4 (sym4) Discrete Wavelet Transform and SP, CoSaMP and ALISTA Algorithm
Issue:
Volume 11, Issue 3, September 2025
Pages:
49-56
Received:
1 October 2025
Accepted:
14 October 2025
Published:
31 October 2025
Abstract: This work proposes an efficient image reconstruction method based on compressive sensing (CS), combining the level-3 Symlet 4 (sym4) discrete wavelet transform with three iterative reconstruction algorithms: Subspace Pursuit (SP), Compressive Sampling Matched Pursuit (CoSaMP), and Analytic Learned Iterative Shrinkage Thresholding Algorithm (ALISTA). The approach follows four key steps: (1) decomposing the original image via the sym4 wavelet transform to obtain a sparse representation, (2) performing compressed sampling using a random measurement matrix, (3) reconstructing the sparse signal from the reduced measurements using one of the three optimization algorithms, and (4) recovering the final image through the inverse wavelet transform. Experimental evaluation uses the standard Lena image (200 × 200 pixels) and compares the performance of the three algorithms according to two criteria: reconstruction quality (measured by SSIM) and computational cost (reconstruction time in minutes), across sampling rates ranging from 10% to 60%. Results show that all three methods achieve very similar SSIM scores (up to >0.96 at 60%), indicating high structural fidelity regardless of the algorithm chosen. However, ALISTA stands out significantly for its temporal efficiency, particularly at low sampling rates (<0.1 minute at 10%), while CoSaMP exhibits high and unstable computation times (peaking at ~34 minutes at 40%). SP offers a stable, nearly linear increase in runtime but remains consistently slower than ALISTA. These results demonstrate that ALISTA provides the best trade-off between quality and speed. Thus, this study validates the value of coupling the sym4 wavelet basis with modern, learned optimization algorithms for practical CS applications in image processing, where computational efficiency is as critical as reconstruction accuracy.
Abstract: This work proposes an efficient image reconstruction method based on compressive sensing (CS), combining the level-3 Symlet 4 (sym4) discrete wavelet transform with three iterative reconstruction algorithms: Subspace Pursuit (SP), Compressive Sampling Matched Pursuit (CoSaMP), and Analytic Learned Iterative Shrinkage Thresholding Algorithm (ALISTA)...
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Research Article
Spatio-temporal Dynamics of Rainfall and Temperature in Addis Ababa: Variability, Trends, and Extremes
Zelalem Alemayehu Kebede*
,
Geberemariyam Adane,
Tarekegn Abera
Issue:
Volume 11, Issue 3, September 2025
Pages:
57-71
Received:
11 November 2025
Accepted:
25 November 2025
Published:
26 December 2025
DOI:
10.11648/j.ijdst.20251103.12
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Abstract: Understanding the temporal and spatial variability of rainfall and temperature is essential for climate adaptation and urban planning in rapidly growing cities. This study examines the monthly, seasonal, and annual patterns of rainfall and temperature in Addis Ababa, Ethiopia, over the period 1981-2022, using high-resolution 4 km gridded climate datasets. Trends were assessed using Modified Mann-Kendall (MMK) and Sen’s slope estimators, while inter-annual variability was quantified using the Standardized Anomaly Index (SAI) and coefficient of variation (CV). Extreme temperature events were evaluated based on the 90th percentile for heat stress and the 10th percentile for cold stress. The analysis shows that Addis Ababa experiences its highest rainfall during the Kiremt season, particularly in the northern and northeastern parts, while the Belg season contributes moderate rainfall, and Bega remains the driest season. Mean monthly temperatures peak between March and May, whereas cooler conditions prevail from July through January. Spatially, southern, central, and eastern areas are consistently warmer, while northern and northwestern sectors are cooler. Trend analyses indicate no statistically significant changes in rainfall, although a slight decline in Belg and a modest increase in Kiremt are observed. Conversely, both maximum (Tmax) and minimum (Tmin) temperatures show significant increasing trends across all seasons and annually, with Tmax rising most rapidly during Belg (0.085°C yr-1) and Tmin increasing sharply in Bega (0.065°C yr-1). SAI and CV analyses highlight high inter-annual and seasonal variability in rainfall, whereas temperature variability remains relatively low, underscoring rainfall as the dominant driver of climate variability. Extreme temperature assessment reveals that heat stress events are concentrated in Belg, while cold stress is most pronounced during Bega. These findings provide a comprehensive overview of rainfall and temperature dynamics, trends, and extremes in Addis Ababa, offering essential insights for climate adaptation, urban planning, and resource management. The observed warming trends, combined with spatial and seasonal variability, emphasize the need for targeted monitoring and adaptive strategies to mitigate climate-related risks in the city.
Abstract: Understanding the temporal and spatial variability of rainfall and temperature is essential for climate adaptation and urban planning in rapidly growing cities. This study examines the monthly, seasonal, and annual patterns of rainfall and temperature in Addis Ababa, Ethiopia, over the period 1981-2022, using high-resolution 4 km gridded climate da...
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