Intelligent noise detection and filtering using neuro-fuzzy system

In this paper, a new method for the identification and removal of random-valued impulse noise (RVIN) from images is proposed. We propose to identify the central pixel of the current sliding window as a noisy or noise free pixel based on the similar local statistics of the current window. Our proposed RVIN identifier works in an iterative way. Pixel identified as a noisy pixel is replaced by proposed minimum difference similar value in an optimal directions. The performance of the proposed method is evaluated on different test images and compared with state-of-the-art methods. Experimental results show that the proposed method cannot only identify the impulse noise efficiently, but can also preserve the detailed information of an image.

Section: Dataset and Evaluation Criterionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Removal of random-valued impulse noise by local statistics

Dawood

Guo

2014

“…Since (27) is equivalent to (19), parameters a ij can be easily found from these two equations, which are as follows:…”

Section: Consequent Parameters Learning By a Linear Svmmentioning

confidence: 99%

An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

Chen

et al. 2014

In this paper, an interval type-2 Takagi-Sugeno fuzzy classification system (IT2T-SFCS) learned by particle swarm optimization (PSO) and support vector machine (SVM) for antecedent and consequent parameters optimization is proposed. The IT2T-SFCS is constructed by fuzzy if-then rules whose antecedents are interval type-2 fuzzy sets and consequents are linear state equations. The antecedents of IT2T-SFCS use the fuzzy iterative self-organizing data analysis technique (ISODATA) and PSO to learn and calculate the optimal centers and the uncertain widths of the Gaussian membership functions. Consequent parameters in IT2T-SFCS are learned through SVM for the purpose of achieving higher generalization ability. The proposed IT2T-SFCS is able to directly handle uncertainties, minimize the effects of uncertainties and get the better generalization performance, which inherits the benefits of interval type-2 T-S fuzzy system and SVM. For demonstration, IT2T-SFCS is used as a classifier in gender recognition. The experimental results show that the performance of the proposed IT2T-SFCS is superior to that of the previous mainstream classifiers.

“…Moreover, the application of fuzzy logic in noise rectification is also gaining more attraction [16,22]. In this paper, the estimation of regularization parameter is based on image discontinuities as a spatial activity.…”

Section: Introductionmentioning

confidence: 99%

Modified particle swarm optimization and fuzzy regularization for pseudo de-convolution of spatially variant blurs

Bilal

Mujtaba

Jaffar

2015

We propose a modified particle swarm optimization (MPSO) based method for Pseudo De-convolution of the ill-posed inverse problem namely, the space-variant image degradation (SVD). In this paper, SVD is simulated by the pseudo convolution of different sub-regions of the image with different known blurring kernels and additive random noise with unknown variance. Two heuristic modifications are proposed in PSO: 1) Initialization of the swarm and 2) Mutation of the global best. Fuzzy logic is applied for the computation of regularization parameter (RP) to cater for the sensitivity of the problem. The computation of RP is crucial due to the additive noise in the SVD image. Thus mathematical morphology (MM) is applied for better extraction of spatial activity from the distorted image. The performance of the proposed method is evaluated with different test images and noise powers. Comparative analysis demonstrates the superiority of proposed restoration, in terms of quantitative measures, over well-known existing and state-of-the-art SVD approaches.