White Gaussian Noise Removal From Computed Tomography Images Using Python

Bhonsle, Devanand; Pillai, Anu G.; Rizvi, Tanu; Mishra, Ravi; Sahu, Anil Kumar; Mishra, Rama

doi:10.1109/icaect60202.2024.10468832

Cited by 1 publication

(1 citation statement)

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“…DTCWT captures features at various scales and orientations using two wavelet filters in a dyadic tree structure [29]. This dual-tree arrangement is shown in Figure 2, with one set shifted by a half sample, yields complex-valued signals, allowing representation of both phase and magnitude information in the signal [30]. Iterative decomposition produces a multiresolution of an input signal denoted by x(n) which is under sampled with a two factor at each stage.…”

Section: Compressed Sensing Based Dual Tree Complex Wavelet Transformmentioning

confidence: 99%

Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm

Nalumansi,

Mwangi,

Kamucha

2024

IJEER

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One powerful technique that can offer a thorough examination of the body's internal structure is magnetic resonance imaging (MRI). MRI's lengthy acquisition times, however, may restrict its clinical usefulness, particularly in situations where time is of the essence. Compressed sensing (CS) has emerged as a potentially useful method for cutting down on MRI acquisition times; nevertheless, the effectiveness of CS-MRI is dependent on the selection of the sparsity-promoting algorithm and sampling scheme. This research paper presents a novel method based on adaptive multi-extreme particle swarm optimization (AMEPSO) and dual tree complex wavelet transform (DTCWT) for fast image acquisition in magnetic resonance. The method uses AMEPSO in order to maximize the sampling pattern and minimize reconstruction error, while also exploiting the sparsity of MR images in the DTCWT domain to improve directional selectivity and shift invariance. MATLAB software was used for simulation of the proposed method. In comparison with the particle swarm optimized-DTCWT (PSODTCWT) and DTCWT algorithms, respectively, the results demonstrated an improvement in the peak signal-to-noise ratio of 8.92% and 15.92% and a higher structural similarity index measure of 3.69% and 7.5%. Based on these improvements, the proposed method could potentially make high-quality, real-time MRI imaging possible, which might improve detection and treatment of medical conditions and increase the throughput of MRI machines.

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Section: Compressed Sensing Based Dual Tree Complex Wavelet Transformmentioning

confidence: 99%