Turbulent-PSO-Based Fuzzy Image Filter With No-Reference Measures for High-Density Impulse Noise

doi:10.1109/TSMCB.2012.2205678

Home
/
Papers
/
Turbulent-PSO-Based Fuzzy Image Filter With No-Reference Measures for High-Density Impulse Noise

Journal Article•DOI•

Turbulent-PSO-Based Fuzzy Image Filter With No-Reference Measures for High-Density Impulse Noise

Hsien-Hsin Chou¹, Ling-Yuan Hsu², Hwai-Tsu Hu¹•Institutions (2)

National Ilan University¹, National Taiwan University of Science and Technology²

01 Feb 2013-IEEE Transactions on Systems, Man, and Cybernetics (IEEE)-Vol. 43, Iss: 1, pp 296-307

TL;DR: The experimental results confirm that the TPFF attains an excellent quality of restored images in terms of peak signal-to-noise ratio, mean square error, and mean absolute error even when the noise rate is above 0.5 and without the aid of noise-free images.

read less

Abstract: Digital images are often corrupted by impulsive noise during data acquisition, transmission, and processing. This paper presents a turbulent particle swarm optimization (PSO) (TPSO)-based fuzzy filtering (or TPFF for short) approach to remove impulse noise from highly corrupted images. The proposed fuzzy filter contains a parallel fuzzy inference mechanism, a fuzzy mean process, and a fuzzy composition process. To a certain extent, the TPFF is an improved and online version of those genetic-based algorithms which had attracted a number of works during the past years. As the PSO is renowned for its ability of achieving success rate and solution quality, the superiority of the TPFF is almost for sure. In particular, by using a no-reference Q metric, the TPSO learning is sufficient to optimize the parameters necessitated by the TPFF. Therefore, the proposed fuzzy filter can cope with practical situations where the assumption of the existence of the “ground-truth” reference does not hold. The experimental results confirm that the TPFF attains an excellent quality of restored images in terms of peak signal-to-noise ratio, mean square error, and mean absolute error even when the noise rate is above 0.5 and without the aid of noise-free images.

...read moreread less

Citations

PDF

Open Access

More filters

Journal Article•DOI•

No-Reference Image Blur Assessment Based on Discrete Orthogonal Moments

[...]

Leida Li¹, Weisi Lin², Xuesong Wang¹, Gaobo Yang³, Khosro Bahrami², Alex C. Kot² - Show less +2 more•Institutions (3)

China University of Mining and Technology¹, Nanyang Technological University², Hunan University³

01 Jan 2016-IEEE Transactions on Systems, Man, and Cybernetics

TL;DR: The experimental results demonstrate that the proposed blind image blur evaluation algorithm can produce blur scores highly consistent with subjective evaluations and outperforms the state-of-the-art image blur metrics and several general-purpose no-reference quality metrics.

...read moreread less

Abstract: Blur is a key determinant in the perception of image quality. Generally, blur causes spread of edges, which leads to shape changes in images. Discrete orthogonal moments have been widely studied as effective shape descriptors. Intuitively, blur can be represented using discrete moments since noticeable blur affects the magnitudes of moments of an image. With this consideration, this paper presents a blind image blur evaluation algorithm based on discrete Tchebichef moments. The gradient of a blurred image is first computed to account for the shape, which is more effective for blur representation. Then the gradient image is divided into equal-size blocks and the Tchebichef moments are calculated to characterize image shape. The energy of a block is computed as the sum of squared non-DC moment values. Finally, the proposed image blur score is defined as the variance-normalized moment energy, which is computed with the guidance of a visual saliency model to adapt to the characteristic of human visual system. The performance of the proposed method is evaluated on four public image quality databases. The experimental results demonstrate that our method can produce blur scores highly consistent with subjective evaluations. It also outperforms the state-of-the-art image blur metrics and several general-purpose no-reference quality metrics.

...read moreread less

239 citations

Cites background from "Turbulent-PSO-Based Fuzzy Image Fil..."

...Therefore, blind IQA metrics potentially have more applications in real-world scenarios [16]....
[...]

Journal Article•DOI•

Intuitionistic fuzzy interaction bonferroni means and its application to multiple attribute decision making.

[...]

Yingdong He¹, Zhen He¹, Huayou Chen²•Institutions (2)

College of Management and Economics¹, Anhui University²

01 Jan 2015-IEEE Transactions on Systems, Man, and Cybernetics

TL;DR: The detailed steps of multiple attribute decision making with the presented operators under intuitionistic fuzzy environment are investigated and an example is illustrated to show the validity and feasibility of the new approach.

...read moreread less

Abstract: The Bonferroni mean (BM) was originally presented by Bonferroni and had been generalized by many researchers for its capacity to capture the interrelationship between input arguments. Nevertheless, the existing intuitionistic fuzzy BMs only consider the effects of membership function or nonmembership function of different intuitionistic fuzzy sets (IFSs). As complements to the existing generalizations of BM under intuitionistic fuzzy environment, this paper also considers the interactions between the membership function and nonmembership function of different IFSs and develops the intuitionistic fuzzy interaction BM and the weighted intuitionistic fuzzy interaction BM. We investigate the properties of these new extensions of BM and discuss their special cases. Furthermore, the detailed steps of multiple attribute decision making with the presented operators under intuitionistic fuzzy environment are investigated and an example is illustrated to show the validity and feasibility of the new approach.

...read moreread less

101 citations

Cites background from "Turbulent-PSO-Based Fuzzy Image Fil..."

...Besides, vA11 , vA12 , vA13 , vA11 vA12 , vA11 vA13 , vA12 vA13 are also considered by the power operation in [2] and [8]....
[...]
...Thus, compared with the operational laws in [2] and [8], for any three IFNs A11, A12, and A13, the new operational laws also take the effects of uA11 vA12 , vA11 uA12 , uA11 vA13 , uA12 vA13 , vA11 uA13 , vA12 uA13 , uA11 vA12 vA13 , vA11 uA12 vA13 , uA11 uA12 vA13 vA11 vA12 uA13 , uA11 vA12 uA13 , vA11 uA12 uA13 ....
[...]
...AGGREGATION operators, usually taking the forms of mathematic functions, are of great importance to the application of multiple attribute decision making, decision support, and recommender systems, which have got a lot of attentions [4], [6], [8]–[10], [13], [15]–[19], [25], [28]–[30], [32]–[36]....
[...]

Journal Article•DOI•

Multilayer Obstacle-Avoiding X-Architecture Steiner Minimal Tree Construction Based on Particle Swarm Optimization

[...]

Genggeng Liu¹, Xing Huang¹, Wenzhong Guo¹, Yuzhen Niu¹, Guolong Chen¹ - Show less +1 more•Institutions (1)

Fuzhou University¹

01 May 2015-IEEE Transactions on Systems, Man, and Cybernetics

TL;DR: An effective algorithm based on particle swarm optimization is presented to construct a multilayer obstacle-avoiding X-architecture SMT (ML-OAXSMT), which is the first work to address this problem and can offer the theory supports for chip design based on non-Manhattan architecture.

...read moreread less

Abstract: As the basic model for very large scale integration routing, the Steiner minimal tree (SMT) can be used in various practical problems, such as wire length optimization, congestion, and time delay estimation. In this paper, an effective algorithm based on particle swarm optimization is presented to construct a multilayer obstacle-avoiding X-architecture SMT (ML-OAXSMT). First, a pretreatment strategy is presented to reduce the total number of judgments for the routing conditions around obstacles and vias. Second, an edge transformation strategy is employed to make the particles have the ability to bypass the obstacles while the union-find partition is used to prevent invalid solutions. Third, according to the feature of ML-OAXSMT problem, we design an edge-vertex encoding strategy, which has the advantage of simple and effective. Moreover, a penalty mechanism is proposed to help the particle bypass the obstacles, and reduce the generation of via at the same time. Experimental results show that our algorithm from a global perspective of multilayer structure can achieve the best solution quality among the existing algorithms. Finally, to our best knowledge, we redefine the edge cost and then construct the obstacle-avoiding preferred direction X-architecture Steiner tree, which is the first work to address this problem and can offer the theory supports for chip design based on non-Manhattan architecture.

...read moreread less

89 citations

Journal Article•DOI•

Self-evolving type-2 fuzzy brain emotional learning control design for chaotic systems using PSO

[...]

Tien-Loc Le¹, Chih-Min Lin¹, Tuan-Tu Huynh¹•Institutions (1)

Yuan Ze University¹

01 Dec 2018-Applied Soft Computing

TL;DR: A design of interval type-2 fuzzy brain emotional learning control (T2FBELC) combining with the self-evolving algorithm to help the network to automatically achieve the optimum construction from the empty initial rule is presented.

...read moreread less

61 citations

Journal Article•DOI•

Real-Time Impulse Noise Suppression from Images Using an Efficient Weighted-Average Filtering

[...]

Hossein Hosseini¹, Farzad Hessar¹, Farokh Marvasti²•Institutions (2)

Sharif University of Technology¹, University of Washington²

10 Jul 2014-arXiv: Computer Vision and Pattern Recognition

TL;DR: Experimental results show that the proposed method outperforms the best existing methods in both PSNR measure and visual quality and is quite suitable for real-time applications.

...read moreread less

Abstract: In this paper, we propose a method for real-time high density impulse noise suppression from images. In our method, we first apply an impulse detector to identify the corrupted pixels and then employ an innovative weighted-average filter to restore them. The filter takes the nearest neighboring interpolated image as the initial image and computes the weights according to the relative positions of the corrupted and uncorrupted pixels. Experimental results show that the proposed method outperforms the best existing methods in both PSNR measure and visual quality and is quite suitable for real-time applications.

...read moreread less

51 citations

Cites methods from "Turbulent-PSO-Based Fuzzy Image Fil..."

...For median-based filters, fuzzy-based algorithms, ad-hoc ideas and weighted-average filters, DBA [3], TPFF [13], EPA [5] and SAWM [7] have the best results, respectively....
[...]
...Also, due to the nature of impulse noise, some methods are proposed based on fuzzy logics, such as Detail-Preserving Filter (DPF) [6], Noise Adaptive Fuzzy Switching Median (NAFSM) filter [8], and Turbulent Particle swarm optimization based Fuzzy Filtering (TPFF) [13]....
[...]

1
2
3
4
…
5
6
7
8
9

Collapse

References

PDF

Open Access

More filters

Book•

Fuzzy sets

[...]

Lotfi A. Zadeh

01 Aug 1996

TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.

...read moreread less

Abstract: A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.

...read moreread less

52,705 citations

Proceedings Article•DOI•

Particle swarm optimization

[...]

James Kennedy, Russell C. Eberhart

06 Aug 2002

TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.

...read moreread less

Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.

...read moreread less

35,104 citations

"Turbulent-PSO-Based Fuzzy Image Fil..." refers background or methods in this paper

...technique developed by Eberhart and Kennedy [21], [22] based on the social behavior of birds flocking for food searching....
[...]
...James Kennedy and Russ Eberhart in 1995 [22], numerous applications of the basic algorithm have been developed in the literature....
[...]

Proceedings Article•DOI•

A new optimizer using particle swarm theory

[...]

Russell C. Eberhart¹, James Kennedy•Institutions (1)

Purdue University¹

04 Oct 1995

TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.

...read moreread less

Abstract: The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.

...read moreread less

14,477 citations

"Turbulent-PSO-Based Fuzzy Image Fil..." refers methods in this paper

...[22] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc....
[...]
...[35] M. Clerc and J. Kennedy, “The particle swarm—Explosion, stability, and convergence in a multidimensional complex space,” IEEE Trans....
[...]
...Since the introduction of the particle swarm optimizer by James Kennedy and Russ Eberhart in 1995 [22], numerous applications of the basic algorithm have been developed in the literature....
[...]
...It is a popular computational technique developed by Eberhart and Kennedy [21], [22] based on the social behavior of birds flocking for food searching....
[...]
...[21] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proc....
[...]

Proceedings Article•DOI•

A modified particle swarm optimizer

[...]

Yuhui Shi¹, Russell C. Eberhart¹•Institutions (1)

Indiana University – Purdue University Indianapolis¹

04 May 1998

TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.

...read moreread less

Abstract: Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic" operations, such as mutation, crossover and reproduction. A best solution is evolved through the generations. In contrast to evolutionary computation techniques, Eberhart and Kennedy developed a different algorithm through simulating social behavior (R.C. Eberhart et al., 1996; R.C. Eberhart and J. Kennedy, 1996; J. Kennedy and R.C. Eberhart, 1995; J. Kennedy, 1997). As in other algorithms, a population of individuals exists. This algorithm is called particle swarm optimization (PSO) since it resembles a school of flying birds. In a particle swarm optimizer, instead of using genetic operators, these individuals are "evolved" by cooperation and competition among the individuals themselves through generations. Each particle adjusts its flying according to its own flying experience and its companions' flying experience. We introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the significant and effective impact of this new parameter on the particle swarm optimizer.

...read moreread less

9,373 citations

"Turbulent-PSO-Based Fuzzy Image Fil..." refers methods in this paper

...[32] Y. Shi and R. C. Eberhart, “A modified particle swarm optimizer,” in Proc....
[...]
...ω represents an inertia weight employed as an improvement proposed by Shi and Eberhart [32]....
[...]
...[22] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc....
[...]
...Since the introduction of the particle swarm optimizer by James Kennedy and Russ Eberhart in 1995 [22], numerous applications of the basic algorithm have been developed in the literature....
[...]
...In each iteration, the PSO proceeds with vtkd =ω × vt−1kd + c1 × rand1()× ( ppd − pt−1kd ) + c2 × rand2()× ( pgd − pt−1kd ) (11) ptkd = p t−1 kd + v t kd (12) where c1 and c2 are positive learning rates which respectively determine the relative influence of the cognition and social components. rand1() and rand2() are uniformly distributed random numbers between 0 and 1. ω represents an inertia weight employed as an improvement proposed by Shi and Eberhart [32]....
[...]

Journal Article•DOI•

The particle swarm - explosion, stability, and convergence in a multidimensional complex space

[...]

M. Clerc¹, James Kennedy²•Institutions (2)

Orange S.A.¹, Bureau of Labor Statistics²

01 Feb 2002-IEEE Transactions on Evolutionary Computation

TL;DR: This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.

...read moreread less

Abstract: The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. This paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functions.

...read moreread less

8,287 citations