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In many applications, image and video signals are corrupted by impulse noise during acquisition or transmission. Hence there is a need for an efficient and consumer friendly impulse noise removal technique. In this paper, an efficient low cost VLSI architecture for the edge preserving impulse noise removal technique has been proposed. The architecture comprises of two line buffers, register banks,...
A novel fourth order PDE model for image denoising is presented in this paper. Gaussian curvature is integrated into the construction of geometrical energy functional within within the geometrical framework for image processing. The corresponding time dependent PDE model is a combination of three fourth order terms and a third order term with some feasible constrains. Image denoising is attained by...
Based on the research and analysis of the evolution process of three different level models, a concept of anisotropic diffusion equation which gathers all the majority of them is introduced. Furthermore, we propose the definition of UPDE (Universal Partial Differential Equations)-a more general equation, which is better in diffusion filtering and also keeps the edge's information in an image at the...
A new nonlinear iterative average filtering technique for removing random impulse noise from images is presented. It can be used for both grayscale and color images. Filtering results of the standard test images and comparisons with results of other denoising methods are presented. The authors' approach offers especially good results for strongly corrupted images (corruption pixel ratio above 40%).
This paper proposes an efficient algorithm for removing noise from corrupted images by incorporating a wavelet based thresholding with a spatial based joint bilateral filter. Although wavelet-based methods are efficient in image denoising, they are prone to producing low-frequency noise and edge ringing which relate to the structure of the underlying wavelet. On the other hand, most spatial-based...
A dynamic stochastic resonance (DSR)-based technique in discrete wavelet transform (DWT) domain for noise suppression in digital images has been proposed in this paper. The initial results on investigation of this concept for denoising of images corrupted by gaussian noise have been presented. Though traditionally noise is considered as undesirable, it has been utilized in the proposed technique to...
Image denoising plays a fundamental role in many image processing applications. Utilizing sparse representation and nonlocal averaging together is such a successful framework that leads to considerable progress in denoising. Almost all the newly proposed denoising algorithms are built base on it, different in detailed implementation, and the denoising performance seems converging. What is the denoising...
We propose an effective image denoising method by revising the conventional “nonlocal means (NL-means)” method. Conventional NL-means replaces a noisy pixel by the weighted average of other reference pixels depending on the similarity between local neighborhoods of target pixel and reference pixels. Noise, however, reduces the similarity even within the same pattern blocks. This is due to the weighted...
In this paper the authors present a novel variational method for the reconstruction of images corrupted by non-uniformly distributed noise. The additive noise model has been studied extensively, using variational techniques such as the Rudin, Osher and Fatemi model. However, the reconstruction of images corrupted by non-Gaussian noise has not yet been thoroughly studied. The proposed model includes...
A novel method to detect and remove adherent noises in videos from a moving camera is presented in this paper. The basic idea is to detect the adherent noises by spatio-temporal image processing technology first and then remove and restore the information in noise regions using a 3D inpainting technology. Under the condition that camera motion is unknown and unconstrained, a 3D spatio-temporal image...
In many practical cases of image processing, only a noisy image is available. Many image denoising methods usually require the exact value of the noise distribution as an essential filter parameter. However, to estimate the noise solely from the information of the noisy image is a difficult task. A simple but accurate noise estimator would significantly benefit many image denoising methods. In this...
Recently, investigations on medical imaging have been indicating a strong correlation between cases of cancers and the increasing number of Computed Tomography (CT) exams, mainly due to high radiation doses to which patients are exposed during the data acquisition process. Thus, there is a need to reduce the radiation doses whereas still keeping satisfactory quality images for diagnosis. In this paper,...
This paper describes a simple image noise removal method which combines a preprocessing step with the Yaroslavsky filter for strong numerical, visual, and theoretical performance on a broad class of images. The framework developed is a two-stage approach. In the first stage the image is denoised by a classical denoising method (e.g., wavelet or curvelet thresholding). In the second step a modification...
Along with the development of information technology, digital signal filled with the whole world, we see the image can be converted to be computer to deal with a digital signal. Digital image processing is through computer tool, with digital image signal by a series of processing operations, and get people with the needs of the application. This paper introduces the Fourier transform and wavelet transform...
Unmanned Aerial Vehicles (UAV) digital images are often badly degraded by noise during dynamic acquisition and transmission process. Denoising is very important and difficult for UAV-vision Guided, because natural scene image is complicated and having lots of the edges and texture details. The image denoising algorithm based on adaptive dual-tree discrete wavelet packets(ADDWP) which combine the dual-tree...
The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on. In this paper, we propose a hyperspectral image denoising algorithm with a spatial and spectral fusion strategy. The idea is to denoise the noisy hyperspectral 3D cube using a given 2D denoising algorithm but applied from...
Sparse representation has been applied to image denoising in recent years. It is based on the assumption that the non-noise component in the signal can be approximated by only a small number of atoms in a dictionary while the noise component cannot. Previous researches have shown its excellent ability of noise reduction for images with signal-independent Gaussian noise. However, hyperspectral imagery...
This paper proved a new adaptive threshold in spherical coordinate system based on Besov space norm theory for application of the internet of things (IOT). It presented a new adaptive curve shrinkage function to overcome the limitation of translational functions. The new function could reach and exceed the true value and enhance the image edge. According to the image statistical characteristics in...
This paper presents a new fractional variational model for removing multiplicative noise in ultrasound images. This model not only inherits the perfect denoising effects of total variational models, but also retains texture details of smooth regions owing to the characteristics of fractional calculus. The experimental results demonstrate that the quality of images denoised by the proposed model is...
Steerable Pyramid is a flexible wavelet decomposition method. The statistics of the wavelet coefficients is non-Gaussian, which follow the generalized Gaussian distribution (GGD). In this paper, we exploit the GGD as the modeling of the sub-band coefficients and estimate the parameter corresponding. We use the generalized non-local means expression and propose an generalized non-local means algorithm...
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