Improved Adaptive Median Filter Algorithm for Removing Impulse Noise from Grayscale Images

Tang, Ruiguang; Zhou, Xiao; Wang, Dongyan

doi:10.5829/ije.2017.30.10a.11

Cited by 2 publications

(1 citation statement)

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“…A new framework for image enhancement is proposed by Iravani and Ezoji [23] using optimization theorem. The quality of the image was improved by Tang et al [24], Lin and Feng [25] based on image denoising. Mortezaei et al [26] introduced an adaptive un-sharp masking algorithm to enhance image quality.…”

Section: Introductionmentioning

confidence: 99%

Image Restoration by Projection onto Convex Sets with Particle Swarm Parameter Optimization

Rashno

Fadaei

2023

IJE

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Image restoration is the operation of obtaining a high-quality image from a corrupt/noisy image and is widely used in many applications such as Magnetic Resonance Imaging (MRI) and fingerprint identification. This paper proposes an image restoration model based on projection onto convex sets (POCS) and particle swarm optimization (PSO). For this task, a number of convex sets are used as constraints and images are projected to these sets iteratively to reach restored image. Since relaxation parameter in POCS has a significant effect on restoration results, PSO is developed to find the best value for this parameter to be used in restoration process. The proposed scheme for image restoration is evaluated on three popular images with 4 configurations of noise, compared with 5 competitive restoration models. Results demonstrate that the proposed method outperforms other models in 32 out of 48 cases in images with different noise configurations with respect to relative error, ISNR, MAE and MSE measures.

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Section: Introductionmentioning

confidence: 99%