Application In Image Denoising Research Articles

Statistics of Natural Stochastic Textures and Their Application in Image Denoising.

Natural stochastic textures (NSTs), characterized by their fine details, are prone to corruption by artifacts, introduced during the image acquisition process by the combined effect of blur and noise. While many successful algorithms exist for image restoration and enhancement, the restoration of natural textures and textured images based on suitable statistical models has yet to be further improved. We examine the statistical properties of NST using three image databases. We show that the Gaussian distribution is suitable for many NST, while other natural textures can be properly represented by a model that separates the image into two layers; one of these layers contains the structural elements of smooth areas and edges, while the other contains the statistically Gaussian textural details. Based on these statistical properties, an algorithm for the denoising of natural images containing NST is proposed, using patch-based fractional Brownian motion model and regularization by means of anisotropic diffusion. It is illustrated that this algorithm successfully recovers both missing textural details and structural attributes that characterize natural images. The algorithm is compared with classical as well as the state-of-the-art denoising algorithms.

Compressed Sensing Method Application in Image Denoising

In order to improve the work of a single path network reliability failure case, this chapter put forward a set of a single backup path algorithm based on the path. In this algorithm, using two disjoint paths as work path to transmit data, and USES the path does not intersect with the job of a set the path as the backup path. Then, this algorithm, is proposed based on a single - a single backup path algorithm of shortest path set to ensure data under the case of the second work path of effective transmission. Theoretical derivation and numerical simulation results show that in a single work path failure cases, and the calculation of DMP - compared to BP algorithm, this scheme can greatly improve network reliability, and save the network resources.

Biorthogonal balanced multiwavelets with high armlets order and their application in image denoising

Multiple approaches can be used to meet the nth order wavelet analysis consistency requirement between balanced multiwavelets and armlets. Due to the complexity of the n-balancing conditions, it is difficult to construct a family of n-balanced orthogonal multiwavelets for a relatively large n. In this paper, we introduce a convenient way to construct symmetric/antisymmetric biorthogonal multiwavelets provided that both the balanced order n≥2 and the high armlets order conditions are met. First, we introduce the concept of biorthogonal armlets and illustrate a relationship among the four filter banks of a biorthogonal system. Then we present an algorithm for constructing biorthogonal balanced multiwavelets with armlets order (BBMA) and compute a novel class of such multiwavelets. Finally, we investigate the performances of BBMA and existing wavelets on the application of image denoising. Simulation results show that BBMA typically result in improved denoising.

Research of Image Denoising Method about Wavelet Transform with Neighborhood Average

Because wavelet transform has good time-frequency characteristics, and its application in image denoising has been promising. Firstly, use the threshold method of the wavelet transform is used in removing image noise, and then the denoised image is smoothed using neighborhood average filtering with Gauss template. And wavelet denoising process and domain threshold selection principle are discussed. Simulation results show that this method can effectively reduce the noise and can remain most of image details better.

HPM-Based Dynamic Wavelet Transform and Its Application in Image Denoising

Wavelet-based multiscale interpolation operator is often employed to construct the adaptive numerical method for PDEs, in which the computational complexity of the wavelet transform is one of the main factors affecting the algorithm efficiency. As the wavelet transform just acts as the detector of the characteristic points in the interpolation operator, the multiscale wavelet interpolation operator can be viewed as a nonlinear problem. Based on this assumption, we construct an approximate dynamic interpolation operator with the homotopy perturbation method (HPM), which decreases the computational complexity of the wavelet transform appearing in the wavelet interpolation operator fromO((1/3)42J−1)toO(4J), whereJis the amount of the wavelet scales. Then an adaptive algorithm solving the Perona-Malik model on image denoising is constructed with the HPM-based interpolation operator. Last, the quasi-Shannon wavelet is employed to design the experiments on the medical image and some artificial images denoising. The experiment results show that the simplified wavelet interpolation operator based on HPM possesses the adaptability and nonsensitivity to the time step, which is helpful to improve the algorithm efficiency. This illustrates that the HPM-based wavelet interpolation operator is an effective tool to solve the problems in image processing.

Quaternion Wavelet Analysis and Application in Image Denoising

The quaternion wavelet transform is a new multiscale analysis tool. Firstly, this paper studies the standard orthogonal basis of scale space and wavelet space of quaternion wavelet transform in spatial L2(R2), proves and presents quaternion wavelet’s scale basis function and wavelet basis function concepts in spatial scale space L2(R2; H), and studies quaternion wavelet transform structure. Finally, the quaternion wavelet transform is applied to image denoising, and generalized Gauss distribution is used to model QWT coefficients’ magnitude distribution, under the Bayesian theory framework, to recover the original coefficients from the noisy wavelet coefficients, and so as to achieve the aim of denoising. Experimental results show that our method is not only better than many of the current denoising methods in the peak signal to noise ratio (PSNR), but also obtained better visual effect.

An Adaptive Wavelet Shrinkage and its Application in Image De-Noising

In this paper an adaptive method of shrinkage of the wavelet coefficients is presented. In the method, the wavelet coefficients are divided into two classes by a threshold. One class of them with the smaller absolute values at a scale is transformed with a proportional relation，another class with the larger absolute values at the same scale is transformed with a linear function. The threshold and the coefficient in the proportional relation or in the linear function are determined by the principle of minimizing the Stein’s unbiased risk estimate. In the paper, the method of estimation of the threshold and the coefficient is given and the adaptive method of shrinkage of the wavelet coefficients is applied to image denoising. Examples in the paper show that the presented method has an advantage over SureShrink from the point of view of both the Stein’s unbiased risk estimate and the signal-to-noise ratio. In addition, the method takes almost the same computing time as the SureShrink in image denoising.

Iterative Estimation Algorithms Using Conjugate Function Lower Bound and Minorization-Maximization with Applications in Image Denoising

A fundamental problem in signal processing is to estimate signal from noisy observations. This is usually formulated as an optimization problem. Optimizations based on variational lower bound and minorization-maximization have been widely used in machine learning research, signal processing, and statistics. In this paper, we study iterative algorithms based on the conjugate function lower bound (CFLB) and minorization-maximization (MM) for a class of objective functions. We propose a generalized version of these two algorithms and show that they are equivalent when the objective function is convex and differentiable. We then develop a CFLB/MM algorithm for solving the MAP estimation problems under a linear Gaussian observation model. We modify this algorithm for wavelet-domain image denoising. Experimental results show that using a single wavelet representation the performance of the proposed algorithms makes better than that of the bishrinkage algorithm which is arguably one of the best in recent publications. Using complex wavelet representations, the performance of the proposed algorithm is very competitive with that of the state-of-the-art algorithms.