partition belief median filter based on dempster shafer theory for image processing a novel median type filter controlled by evidence fusion is proposed for removing noise from images the fusion of evidence based on the dempster shafer evidence theory providing a way to deal with the uncertainty in the evidence fusion indicates to what extent a noise is considered the filter proposed here is obtained as a weighted sum of the current pixel value and the output of the median filter and the weight is set based on the belief value of the input signal sequence the efficient step like function is used to partition the belief space and the least mean square lms algorithm is applied to obtain the optimal weight for each block moreover to improve the performance the new filter is recursively implemented experimental results have demonstrated that the proposed filter can outperform many well accepted median based filters in preserving image details while effectively suppressing impulsive noises and it also works satisfactorily in reducing gaussian as well as the mixture of gaussian and impulsive noise

symmetry aware graph matching visual symmetry encodes middle to high level geometric information and plays an important role in many computer vision applications not much effort however has been devoted to utilize symmetry information for graph matching in this paper we propose a new framework for symmetry aware graph matching in the context of image matching in the framework we first define symmetry matrices to characterize the geometric symmetry relation among image features and then develop two methods to discover such symmetry after that we design general strategies to boost graph matching algorithms with symmetry constraints and apply these strategies to several state of the art algorithms for evaluation the proposed symmetry aware graph matching algorithms are first applied to two datasets involving clear symmetry patterns a recently proposed architecture image set and a dental panoramic radiograph collection the results clearly demonstrate the benefits of using symmetry information an experiment is also conducted on a dataset where symmetry is less obvious the results show that our strategies still achieve better of similar performance compared with state of the arts highlightsit is the first time symmetry constraints that are introduced into graph matching the symmetry aware graph matching improves the matching performance a new dataset with annotation for evaluating graph matching is constructed

efficient improvements on the bdnd filtering algorithm for the removal of high density impulse noise switching median filters are known to outperform standard median filters in the removal of impulse noise due to their capability of filtering candidate noisy pixels and leaving other pixels intact the boundary discriminative noise detection bdnd is one powerful example in this class of filters however there are some issues related to the filtering step in the bdnd algorithm that may degrade its performance in this paper we propose two modifications to the filtering step of the bdnd algorithm to address these issues experimental evaluation shows the effectiveness of the proposed modifications in producing sharper images than the bdnd algorithm

application of partition based median type filters for suppressing noise in images an adaptive median based filter is proposed for removing noise from images specifically the observed sample vector at each pixel location is classified into one of m mutually exclusive partitions each of which has a particular filtering operation the observation signal space is partitioned based an the differences defined between the current pixel value and the outputs of cwm center weighted median filters with variable center weights the estimate at each location is formed as a linear combination of the outputs of those cwm filters and the current pixel value to control the dynamic range of filter outputs a location invariance constraint is imposed upon each weighting vector the weights are optimized using the constrained lms least mean square algorithm recursive implementation of the new filter is then addressed the new technique consistently outperforms other median based filters in suppressing both random valued and fixed valued impulses and it also works satisfactorily in reducing gaussian noise as well as mixed gaussian and impulse noise

image denoising a nonlinear robust statistical approach nonlinear filtering techniques based on the theory of robust estimation are introduced some deterministic and asymptotic properties are derived the proposed denoising methods are optimal over the huber spl epsi contaminated normal neighborhood and are highly resistant to outliers experimental results showing a much improved performance of the proposed filters in the presence of gaussian and heavy tailed noise are analyzed and illustrated

a switching median filter with boundary discriminative noise detection for extremely corrupted images a novel switching median filter incorporating with a powerful impulse noise detection method called the boundary discriminative noise detection bdnd is proposed in this paper for effectively denoising extremely corrupted images to determine whether the current pixel is corrupted the proposed bdnd algorithm first classifies the pixels of a localized window centering on the current pixel into three groups lower intensity impulse noise uncorrupted pixels and higher intensity impulse noise the center pixel will then be considered as uncorrupted provided that it belongs to the uncorrupted pixel group or corrupted for that two boundaries that discriminate these three groups require to be accurately determined for yielding a very high noise detection accuracy in our case achieving zero miss detection rate while maintaining a fairly low false alarm rate even up to 70 noise corruption four noise models are considered for performance evaluation extensive simulation results conducted on both monochrome and color images under a wide range from 10 to 90 of noise corruption clearly show that our proposed switching median filter substantially outperforms all existing median based filters in terms of suppressing impulse noise while preserving image details and yet the proposed bdnd is algorithmically simple suitable for real time implementation and application

quasilinear elliptic equations on noncompact riemannian manifolds abstract the existence of solutions to a class of quasilinear elliptic problems on noncompact riemannian manifolds with finite volume is investigated boundary value problems with homogeneous neumann conditions in possibly irregular euclidean domains are included as a special instance a nontrivial solution is shown to exist under an unconventional growth condition on the right hand side which depends on the geometry of the underlying manifold the identification of the critical growth is a crucial step in our analysis and entails the use of the isocapacitary function of the manifold a condition involving its isoperimetric function is also provided

mixed noise removal by weighted encoding with sparse nonlocal regularization mixed noise removal from natural images is a challenging task since the noise distribution usually does not have a parametric model and has a heavy tail one typical kind of mixed noise is additive white gaussian noise awgn coupled with impulse noise in many mixed noise removal methods are detection based methods they first detect the locations of in pixels and then remove the mixed noise however such methods tend to generate many artifacts when the mixed noise is strong in this paper we propose a simple yet effective method namely weighted encoding with sparse nonlocal regularization wesnr for mixed noise removal in wesnr there is not an explicit step of impulse pixel detection instead soft impulse pixel detection via weighted encoding is used to deal with in and awgn simultaneously meanwhile the image sparsity prior and nonlocal self similarity prior are integrated into a regularization term and introduced into the variational encoding framework experimental results show that the proposed wesnr method achieves leading mixed noise removal performance in terms of both quantitative measures and visual quality

a simple neuro fuzzy impulse detector for efficient blur reduction of impulse noise removal operators for digital images a new impulse noise detector based on neuro fuzzy methods is presented the proposed detector comprises two identical neuro fuzzy subdetectors combined with a decision maker the internal parameters of the subdetectors are adaptively adjusted by training training of the subdetectors is accomplished by using a simple computer generated artificial image the detector can be combined with any impulse noise removal operator the operation of the detector is completely independent of the noise removal operator and it has no influence on the filtering behavior of the operator experimental results show that the proposed detector significantly reduces the distortion effects of any impulse noise removal operator even if the operator already has its own noise detector

a new adaptive center weighted median filter for suppressing impulsive noise in images in this work a new adaptive center weighted median acwm filter is proposed for improving the performance of median based filters preserving image details while effectively suppressing impulsive noise the proposed filter is an adaptive cwm filter with an adjustable central weight obtained by partitioning the observation vector space to obtain the optimal weight for each block the efficient scalar quantization sq method is used to partition the observation vector space the center weight within each block is obtained by using a learning approach based on the least mean square lms algorithm the noise filtering procedure is progressively applied through several iterations so that the mean square error of the output can be minimized experimental results have demonstrated that the proposed filter outperforms many well accepted median based filters in terms of both noise suppression and detail preservation the proposed new filter also provides excellent robustness at various percentages of impulsive noise

tuning the smoothness of the recursive median filter the median filter is a special case of nonlinear filters used for smoothing signals since the output of the median filter is always one of the input samples it is conceivable that certain signals could pass through the median filter unaltered these signals define the signature of a filter and are referred to as root signals median filters are known to possess the convergence property meaning that by repeating median filtering a root signal will be found starting from any input signal by associating the nonlinear operation of median filtering with a two terms cost function an optimization process that minimizes that function is obtained cost functions of the same type are associated with different recursive median filtering schemes by replacing the actual values inside the filter s window with the original signal the convergence behavior of these filters and their smoothness are studied by changing the positions of the replacements during filtering a tuning effect of the smoothness is obtained simulation results show that the proposed filtering schemes provide improved performance over the standard recursive median filter succeeding in preserving small details and fine textures

a fuzzy impulse noise detection and reduction method removing or reducing impulse noise is a very active research area in image processing in this paper we describe a new algorithm that is especially developed for reducing all kinds of impulse noise fuzzy impulse noise detection and reduction method fidrm it can also be applied to images having a mixture of impulse noise and other types of noise the result is an image quasi without or with very little impulse noise so that other filters can be used afterwards this nonlinear filtering technique contains two separated steps an impulse noise detection step and a reduction step that preserves edge sharpness based on the concept of fuzzy gradient values our detection method constructs a fuzzy set impulse noise this fuzzy set is represented by a membership function that will be used by the filtering method which is a fuzzy averaging of neighboring pixels experimental results show that fidrm provides a significant improvement on other existing filters fidrm is not only very fast but also very effective for reducing little as well as very high impulse noise

universal impulse noise filter based on genetic programming in this paper we present a novel method for impulse noise filter construction based on the switching scheme with two cascaded detectors and two corresponding estimators genetic programming as a supervised learning algorithm is employed for building two detectors with complementary characteristics the first detector identifies the majority of noisy pixels the second detector searches for the remaining noise missed by the first detector usually hidden in image details or with amplitudes close to its local neighborhood both detectors are based on the robust estimators of location and scale median and mad the filter made by the proposed method is capable of effectively suppressing all kinds of impulse noise in contrast to many existing filters which are specialized only for a particular noise model in addition we propose the usage of a new impulse noise model the mixed impulse noise which is more realistic and harder to treat than existing impulse noise models the proposed model is the combination of commonly used noise models salt and pepper and uniform impulse noise models simulation results show that the proposed two stage gp filter produces excellent results and outperforms existing state of the art filters

fuzzy random impulse noise reduction method a new two step fuzzy filter that adopts a fuzzy logic approach for the enhancement of images corrupted with impulse noise is presented in this paper the filtering method entitled as fuzzy random impulse noise reduction method frinr consists of a fuzzy detection mechanism and a fuzzy filtering method to remove random valued impulse noise from corrupted images based on the criteria of peak signal to noise ratio psnr and subjective evaluations we have found experimentally that the proposed method provides a significant improvement on other state of the art methods

fuzzy two step filter for impulse noise reduction from color images a new framework for reducing impulse noise from digital color images is presented in which a fuzzy detection phase is followed by an iterative fuzzy filtering technique we call this filter the fuzzy two step color filter the fuzzy detection method is mainly based on the calculation of fuzzy gradient values and on fuzzy reasoning this phase determines three separate membership functions that are passed to the filtering step these membership functions will be used as a representation of the fuzzy set impulse noise one function for each color component our proposed new fuzzy method is especially developed for reducing impulse noise from color images while preserving details and texture experiments show that the proposed filter can be used for efficient removal of impulse noise from color images without distorting the useful information in the image

efficient impulse noise reduction via local directional gradients and fuzzy logic this paper proposes an efficient noise reduction method for image restoration a novel pixel classification method by using fuzzy logic and local gradients is first introduced to discriminate noise and noise free pixels from corrupted images then a switching filtering scheme is employed to noisy images the noise pixels will be filtered by a weighted median filter with adaptive weights when the noise free pixels are preserved in the filtered images both numerical comparisons and perceptual observations are demonstrated in the experimental simulations it is found that the proposed method achieves a fairly better performance than a number of other existing methods in noise reduction and image restoration

a hybrid neuro fuzzy filter for edge preserving restoration of images corrupted by impulse noise a new operator for restoring digital images corrupted by impulse noise is presented the proposed operator is a hybrid filter obtained by appropriately combining a median filter an edge detector and a neuro fuzzy network the internal parameters of the neuro fuzzy network are adaptively optimized by training the training is easily accomplished by using simple artificial images that can be generated in a computer the most distinctive feature of the proposed operator over most other operators is that it offers excellent line edge detail and texture preservation performance while at the same time effectively removing noise from the input image extensive simulation experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image

fuzzy random impulse noise removal from color image sequences in this paper a new fuzzy filter for the removal of random impulse noise in color video is presented by working with different successive filtering steps a very good tradeoff between detail preservation and noise removal is obtained one strong filtering step that should remove all noise at once would inevitably also remove a considerable amount of detail therefore the noise is filtered step by step in each step noisy pixels are detected by the help of fuzzy rules which are very useful for the processing of human knowledge where linguistic variables are used pixels that are detected as noisy are filtered the others remain unchanged filtering of detected pixels is done by blockmatching based on a noise adaptive mean absolute difference the experiments show that the proposed method outperforms other state of the art filters both visually and in terms of objective quality measures such as the mean absolute error mae the peak signal to noise ratio psnr and the normalized color difference ncd

impulse noise removal from digital images by a detail preserving filter based on type 2 fuzzy logic a novel image filter based on type 2 fuzzy logic techniques is proposed for detail preserving restoration of digital images corrupted by impulse noise the performance of the proposed filter is evaluated for different test images corrupted at various noise densities and also compared with representative conventional as well as state of the art impulse noise filters from the literature experimental results show that the proposed filter exhibits superior performance over the competing operators and is capable of efficiently suppressing the noise in the image while at the same time effectively preserving thin lines edges texture and other useful information within the image

a detection statistic for random valued impulse noise this paper proposes an image statistic for detecting random valued impulse noise by this statistic we can identify most of the noisy pixels in the corrupted images combining it with an edge preserving regularization we obtain a powerful two stage method for denoising random valued impulse noise even for noise levels as high as 60 simulation results show that our method is significantly better than a number of existing techniques in terms of image restoration and noise detection

tri state median filter for image denoising a novel nonlinear filter called tri state median tsm filter is proposed for preserving image details while effectively suppressing impulse noise we incorporate the standard median sm filter and the center weighted median cwm filter into a noise detection framework to determine whether a pixel is corrupted before applying filtering unconditionally extensive simulation results demonstrate that the proposed filter consistently outperforms other median filters by balancing the tradeoff between noise reduction and detail preservation

wang s multiplicity result for superlinear equations without the ambrosetti rabinowitz condition we consider a nonlinear elliptic equation driven by the sum of a p laplacian and a q laplacian where 1 q 2 p with a p 1 superlinear caratheodory reaction term which doesn t satisfy the usual ambrosetti rabinowitz condition using variational methods based on critical point theory together with techniques from morse theory we show that the problem has at leat three nontrivial solutions among them one is positive and one is negative

a sparse nonnegative matrix factorization technique for graph matching problems graph matching problem that incorporates pairwise constraints can be cast as an integer quadratic programming iqp since it is np hard approximate methods are required in this paper a new approximate method based on nonnegative matrix factorization with sparse constraints is presented firstly the graph matching is formulated as an optimization problem with nonnegative and sparse constraints followed by an efficient algorithm to solve this constrained problem then we show the strong relationship between the sparsity of the relaxation solution and its effectiveness for graph matching based on our model a key benefit of our method is that the solution is sparse and thus can approximately impose the one to one mapping constraints in the optimization process naturally therefore our method can approximate the original iqp problem more closely than other approximate methods extensive and comparative experimental results on both synthetic and real world data demonstrate the effectiveness of our graph matching method we present a graph matching method based on nmf with sparse constraints our sparse model can incorporate the mapping constraints approximately we show the link between the sparsity of the solution and its effectiveness experimental results show the effectiveness of our graph matching method

switching bilateral filter with a texture noise detector for universal noise removal in this paper we propose a switching bilateral filter sbf with a texture and noise detector for universal noise removal operation was carried out in two stages detection followed by filtering for detection we propose the sorted quadrant median vector sqmv scheme which includes important features such as edge or texture information this information is utilized to allocate a reference median from sqmv which is in turn compared with a current pixel to classify it as impulse noise gaussian noise or noise free the sbf removes both gaussian and impulse noise without adding another weighting function the range filter inside the bilateral filter switches between the gaussian and impulse modes depending upon the noise classification result simulation results show that our noise detector has a high noise detection rate as well as a high classification rate for salt and pepper uniform impulse noise and mixed impulse noise unlike most other impulse noise filters the proposed sbf achieves high peak signal to noise ratio and great image quality by efficiently removing both types of mixed noise salt and pepper with uniform noise and salt and pepper with gaussian noise in addition the computational complexity of sbf is significantly less than that of other mixed noise filters

fingerprint reference point detection for image retrieval based on symmetry and variation reference point plays an important role in fingerprint identification systems the reference point is widely used for the fingerprint retrieval in large scale databases this paper proposes a novel algorithm for detecting a convex core point as a unique reference point consistently and accurately for all types of fingerprints in order to detect robust core point candidates a modified complex filter called semi radial symmetry filter is proposed to detect correctly rotational symmetries of core points moreover a vertical orientation variation feature called voriv feature is proposed to remove spurious core points and concave core points therefore the proposed technique computes the variation and symmetry combined energy vscome then the reference point is located by searching the global vscome maximum the experimental results on the public database fvc2004 db1 set a show that the proposed technique exhibits a very high robustness and gets the best performance in comparing with other approaches in literature

weak solution for neumann p q laplacian problem on riemannian manifold abstract we prove the existence of a nontrivial solution for a nonlinear p q laplacian problem with neumann boundary condition on a non compact riemannian manifold the idea is to reduce the problem in variational form which means to consider the critical points of the corresponding euler lagrange functional in an orlicz sobolev space

switching based filter based on dempster s combination rule for image processing in this work a two pass switching rank ordered arithmetic mean tsram filter that preserves image details while effectively suppressing impulse noise is proposed to improve the performance of switching based median filters the proposed filter mechanism includes an efficient impulse detector based on the dempster shafer d s evidence theory and a rank ordered arithmetic mean filter that works by estimating the noise free ordered mean values excluding the current pixel a modified dempster s combination rule is applied to the impulse detector to improve filtering performance a simple switching median filter is included to perform second pass filtering experimental results show that the proposed filter outperforms many well accepted switching based median filters in terms of both noise suppression and detail preservation providing excellent robustness at various percentages of impulse noise

partition fuzzy median filter based on fuzzy rules for image restoration in this paper a novel adaptive median based filter called the partition fuzzy median pfm filter is proposed for improving the median based filter to preserve image details while effectively suppressing impulsive noises the proposed filter achieves its effect through a summation of the weighted output of the median filter and the related weighted input signal the weights are set in accordance with the fuzzy rules in order to design this weight function a method to partition of the observation vector space and a learning approach are proposed so that the mean square error of the filter output can be minimum based on the constrained least mean square algorithm an iterative learning procedure is derived and its convergence property is investigated as for the noise suppressing on both fixed and random valued impulses without degrading the quality of fine details extensive experimental results demonstrate that the proposed filter outperforms the other median based filters in the literature the new filter also provides excellent robustness with respect to various percentages of impulse noise in our testing examples

dirichlet problems for fully anisotropic elliptic equations the existence of a non trivial bounded solution to the dirichlet problem is established for a class of nonlinear elliptic equations involving a fully anisotropic partial differential operator the relevant operator depends on the gradient of the unknown through the differential of a general convex function this function need not be radial nor have a polynomial type growth besides providing genuinely new conclusions our result recovers and embraces in a unified framework several contributions in the existing literature and augments them in various special instances

cognition and removal of impulse noise with uncertainty uncertainties are the major inherent feature of impulse noise this fact makes image denoising a difficult task understanding the uncertainties can improve the performance of image denoising this paper presents a novel adaptive detail preserving filter based on the cloud model cm to remove impulse noise it is called the cm filter first an uncertainty based detector identifies the pixels corrupted by impulse noise then a weighted fuzzy mean filter is applied to remove the noise candidates the experimental results show that compared with the traditional switching filters the cm filter makes a great improvement in image denoising even at a noise level as high as 95 the cm filter still can restore the image with good detail preservation

pde based random valued impulse noise removal based on new class of controlling functions this paper is concerned with partial differential equation pde based image denoising for random valued impulse noise we introduce the notion of eni the abbreviation for edge pixels noisy pixels and interior pixels that denotes the number of homogeneous pixels in a local neighborhood and is significantly different for edge pixels noisy pixels and interior pixels we redefine the controlling speed function and the controlling fidelity function to depend on eni according to our two controlling functions the diffusion and fidelity process at edge pixels noisy pixels and interior pixels can be selectively carried out furthermore a class of second order improved and edge preserving pde denoising models is proposed based on the two new controlling functions in order to deal with random valued impulse noise reliably we demonstrate the performance of the proposed pdes via application to five standard test images corrupted by random valued impulse noise with various noise levels and comparison with the related second order pde models and the other special filtering methods for random valued impulse noise our two controlling functions are extended to automatically other pde models

a universal denoising framework with a new impulse detector and nonlocal means impulse noise detection is a critical issue when removing impulse noise and impulse gaussian mixed noise in this paper we propose a new detection mechanism for universal noise and a universal noise filtering framework based on the nonlocal means nl means the operation is carried out in two stages i e detection followed by filtering for detection first we propose the robust outlyingness ratio ror for measuring how impulselike each pixel is and then all the pixels are divided into four clusters according to the ror values second different decision rules are used to detect the impulse noise based on the absolute deviation to the median in each cluster in order to make the detection results more accurate and more robust the from coarse to fine strategy and the iterative framework are used in addition the detection procedure consists of two stages i e the coarse and fine detection stages for filtering the nl means are extended to the impulse noise by introducing a reference image then a universal denoising framework is proposed by combining the new detection mechanism with the nl means ror nlm finally extensive simulation results show that the proposed noise detector is superior to most existing detectors and the ror nlm produces excellent results and outperforms most existing filters for different noise models unlike most of the other impulse noise filters the proposed ror nlm also achieves high peak signal to noise ratio and great image quality by efficiently removing impulse gaussian mixed noise