Analysis of Particle Swarm Optimization Based 2D FIR Filter for Reduction of Additive and Multiplicative Noise in Images

Muneeswaran, V.; Rajasekaran, M. Pallikonda

doi:10.1007/978-3-319-64419-6_22

Cited by 14 publications

(1 citation statement)

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“…The FIR Filter, on the other hand, operates as an adaptive denoising technique hinging on convolution principles to refine pixel values within an image [17]. Each image pixel is subjected to a specific convolution kernel, encompassing predetermined weightings.…”

Section: B Filtermentioning

confidence: 99%

A Novel Technique for Restoration of Images Subjected to Variable Noise Distribution

Kalaiah J B, Dr. S.N Chandrashekara

2023

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Image restoration plays a pivotal role in numerous applications across diverse fields due to its profound significance in enhancing the quality and interpretability of images. Whether in medical diagnostics, scientific research, surveillance, or historical preservation, the importance of image restoration cannot be overstated. By addressing issues such as noise, blur, and other forms of degradation, this process contributes to the preservation of vital information within images. This research focuses on enhancing image-denoising techniques by employing the Ladner-Fischer Adder (LFA) method. The study explores the effectiveness of LFA in mitigating varying noise distributions, particularly Salt and Pepper Noise (SPN) and Gaussian noise. Through detailed experimentation and analysis, the paper evaluates the performance of LFA-based Median Filtering (LFA-MF) and Finite Impulse Response (LFA-FIR) filtering methods. The results demonstrate substantial improvements in Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM) values for both noise types. Notably, LFA-FIR consistently outperforms LFA-MF in terms of denoising efficacy, highlighting its potential for image restoration in the presence of complex noise patterns. Moreover, the research investigates the ASIC synthesis of LFA, examining parameters such as area, power consumption, and delay across different technology nodes. The findings emphasize the potential of LFA for robust image denoising under diverse noise conditions, offering insights into its applicability and performance in real-world scenarios.

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Section: B Filtermentioning

confidence: 99%