Dissimilar pixel counting based impulse detector for two-phase mixed noise removal

Shi, Zaifeng; Xu, Zehao; Pang, Ke; Cao, Qingjie; Luo, Tiejian

doi:10.1007/s11042-017-4613-1

Cited by 5 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our method is compared with 10 well-known RVIN removal methods all of which are discussed in the introduction. These methods are ACWM [12], SWM [13], DWM [15], ROLD-EPR [19], NSDD [20], ODM [21], FWNLM [22], ℓ0TV-PADMM [24], DPC-INR [25], and AROPD-EPR [26].…”

Section: Resultsmentioning

confidence: 99%

“…Since the resulting optimization problem was non-convex and non-smooth, it was first reformulated as an equivalent mathematical program with equilibrium constraints and then it was solved using a proximal Alternating Direction Method of Multipliers (PADMM). In [25], a new two-phase denoising algorithm (DPC-INR) was implemented using dissimilar pixel counting. In the detection phase, the average difference scheme was used to distinguish whether two neighboring pixels were similar or not, and then the number of dissimilar pixels was compared with a threshold to determine whether the current pixel was noisy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Random-Valued Impulse Noise Detection and Removal based on Local Statistics of Images

Aghajarian¹,

McInroy²

2022

IJACSA

View full text Add to dashboard Cite

Random-valued impulse noise removal from images is a challenging task in the field of image processing and computer vision. In this paper, an effective three-step noise removal method was proposed using local statistics of grayscale images. Unlike most existing denoising algorithms that assume the noise density is known, our method estimated the noise density in the first step. Based on the estimated noise density, a noise detector was implemented to detect corrupted pixels in the second step. Finally, a modified weighted mean filter was utilized to restore the detected noisy pixels while leaving the noise-free pixels unchanged. The noise removal performance of our method was compared with 10 well-known denoising algorithms. Experimental results demonstrated that our proposed method outperformed other denoising algorithms in terms of noise detection and image restoration in the vast majority of the cases.

show abstract