Types Of Noisy Images Research Articles

Noisy image segmentation model combining PDE and improved variational level set method

During image shooting, transmission and acquisition, noise is easily introduced into the image. For pixels in an image, the introduction of noise often changes the intensity values of some pixels, resulting in a large difference in intensity between those pixels and their neighbors. Aiming at the image segmentation problem of weak edge and different types of noise, this paper proposes a new variational horizontal set noise image segmentation model, which is mainly used for the segmentation of target images of different noises. Experimental results show that the traditional PDE segmentation model method does not have obvious segmentation effect on noisy images, while the segmentation method proposed in this paper that combines PDE and improved variational level set has a better segmentation effect on different types of noisy images.

Robust Semisupervised Kernelized Fuzzy Local Information C-Means Clustering for Image Segmentation

To improve the effectiveness and robustness of the existing semisupervised fuzzy clustering for segmenting image corrupted by noise, a kernel space semisupervised fuzzy C-means clustering segmentation algorithm combining utilizing neighborhood spatial gray information with fuzzy membership information is proposed in this paper. The mean intensity information of neighborhood window is embedded into the objective function of the existing semisupervised fuzzy C-means clustering, and the Lagrange multiplier method is used to obtain its iterative expression corresponding to the iterative solution of the optimization problem. Meanwhile, the local Gaussian kernel function is used to map the pixel samples from the Euclidean space to the high-dimensional feature space so that the cluster adaptability to different types of image segmentation is enhanced. Experiment results performed on different types of noisy images indicate that the proposed segmentation algorithm can achieve better segmentation performance than the existing typical robust fuzzy clustering algorithms and significantly enhance the antinoise performance.

Blind Noisy Image Quality Assessment Using Sub-Band Kurtosis.

Noise that afflicts natural images, regardless of the source, generally disturbs the perception of image quality by introducing a high-frequency random element that, when severe, can mask image content. Except at very low levels, where it may play a purpose, it is annoying. There exist significant statistical differences between distortion-free natural images and noisy images that become evident upon comparing the empirical probability distribution histograms of their discrete wavelet transform (DWT) coefficients. The DWT coefficients of low- or no-noise natural images have leptokurtic, peaky distributions with heavy tails; while noisy images tend to be platykurtic with less peaky distributions and shallower tails. The sample kurtosis is a natural measure of the peakedness and tail weight of the distributions of random variables. Here, we study the efficacy of the sample kurtosis of image wavelet coefficients as a feature driving, an extreme learning machine which learns to map kurtosis values into perceptual quality scores. The model is trained and tested on five types of noisy images, including additive white Gaussian noise, additive Gaussian color noise, impulse noise, masked noise, and high-frequency noise from the LIVE, CSIQ, TID2008, and TID2013 image quality databases. The experimental results show that the trained model has better quality evaluation performance on noisy images than existing blind noise assessment models, while also outperforming general-purpose blind and full-reference image quality assessment methods.

Convex‐relaxed active contour model based on localised kernel mapping

Intensity inhomogeneity is one of the major obstacles for intensity-based segmentation in many applications. The recently proposed kernel mapping (KM) method has exhibited excellent performance on segmenting various types of noisy images while it is not effective to handle intensity inhomogeneity. To overcome this drawback, this study presents a localised KM (LKM) method based on the fact that intensity inhomogeneity can be ignored in a local neighbourhood. The authors’ method first reconstructs the KM formulation of image segmentation in a neighbourhood of each pixel, and then such formulations for all pixels can be integrated together to derive the LKM energy functional. Minimisation of the energy functional is implemented by solving an equivalent convex-relaxed problem whose optimisation can be quickly achieved via the split Bregman method. Experimental results on two-phase segmentation and multiphase segmentation demonstrate competitive performance of the LKM method in the presence of intensity inhomogeneity and severe noise.

A cellular automata‐based filtering approach to multi‐temporal image denoising

AbstractThis work addresses the problem of denoising image sequences through an approach that makes use of spatio‐temporal cellular automata‐based filtering. The algorithm is called st‐CAF and one of its key aspects is that the resulting cellular automata contemplate a spatio‐temporal neighbourhood when processing each pixel of the sequence. Additionally, the way the rule sets for the cellular automata are obtained, through evolutionary means, is also relevant, as it allows a good adaptation to any type of image and noise through the appropriate training set. This results in a great advantage over more traditional single frame denoising techniques presented in the literature or even over their adaptation to sequences. A fact that is made relevant in this paper through the application of the algorithm to different types of noisy images and its comparison to other techniques.

Image denoising using generalised Cauchy filter

In many image processing analysis, it is important to significantly reduce the noise level. This study aims at introducing an efficient method for this purpose based on generalised Cauchy (GC) distribution. Therefore, some characteristics of GC distribution is considered. In particular, the characteristic function of a GC distribution is derived by using the theory of positive definite densities and utilising the density of a GC random variable as the characteristic function of a convolution of two generalised non-symmetric Linnik variables. Further, GC distribution is considered as a filter and in the proposed method for image noise reduction the optimal parameters of GC filter is defined by using the particle swarm optimisation. The proposed method is applied to different types of noisy images and the obtained results are compared with four state-of-the-art denoising algorithms. Experimental results confirm that their method could significantly reduce the noise effect.

Vector Image Model to Object Boundary Detection in Noisy Images

A New model is designed for boundary detection and applied it to object segmentation problem in medical images. Our edge following technique incorporates a vector image model and the edge map information. The proposed technique was applied to detect the object boundaries in several types of noisy images where the ill-defined edges were encountered. The proposed techniques performances on object segmentation and computation time were evaluated by comparing with the popular methods, i.e., the ACM, GVF snake models. Several synthetic noisy images were created and tested. The method is successfully tested in different types of medical images including aortas in cardiovascular MR images, and heart in CT images.

Noisy Image Segmentation Based on Artificial Bee Colony Algorithm

Segmentation of noisy images is one of the most challenging problems in image analysis. It hasn’t yet been solved very well. In this paper, we propose a new method for image segmentation, which is able to segment two kinds of noisy images. The experimental results prove that Artificial Bee Colony Algorithm performs better for two types of noisy images.

Anisotropic diffusion filtering for ultrasound speckle reduction

Speckle noise has long been known as a limiting factor for the quality of an ultrasound B-mode image. In this study, anisotropic diffusion filtering is proposed as an effective method for ultrasound speckle reduction. This article provides a brief description of anisotropic diffusion filtering proposed by Perona and Malik, and compares its speckle filtering effects with other filtering methods including median, moving average, and frequency domain Gaussian low-pass. In this study, multiple filters are implemented in Matlab. For each filter, three different types of noisy images with speckle noise are tested. The results show that anisotropic filter can reduce the noise more effectively and meanwhile preserve the boundaries of the objects. In addition, this filter has more controllable filtering parameters and is independent on the information of the noise.