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In this paper, we propose an improved median filtering algorithm by adding filtration function before replacing the value of the median position with the median and doing multiple processing of median filtering to overcome the shortcoming of the traditional Median Filtering Algorithm. Experimental results are shown that an image after processed by the improved algorithm is hard to find image noise,...
This paper presents a novel neural network based patch reordering approach for image denoising and expression. This technique is grounded on estimating the correct location of the patch pixels corrupted by noise using neural network and median filter. The presented work relates the pixel value at the center of the patch with each pixel in the patch window and then estimates the correct location for...
This paper describes a simple and fast way to predict efficiency of DCT-based filtering of images corrupted by signal dependent noise as this often happens for hyperspectral and radar remote sensing. Such prediction allows deciding in automatic way is it worth applying denoising to a given image under condition that parameters of signal-dependent noise are known a priori or pre-estimated with appropriate...
Detection of pixels corrupted by noise and assessing the degree to which the pixels are corrupted intrinsically fuzzy processes, involve uncertainty and imprecision. The paper aims at reconstruction of the image with ensured quality after removing noise from the original image. Here region marking process has been introduced to obtain number of clusters automatically which partition the whole image...
Image denoising is an important in the field of medical image processing and computer vision. Image denoising continues a challenge for researchers because noise removal gives artifacts and the main source for blurring of the images. In this work four different methods are proposed to reduce the image artifacts and noise in the MRI images and also Partial Differential Equations (PDE) is applied to...
Non-local Means(NLM) is increasingly popular in image denoising. In this paper, the nonlocal structure similarity of images obtained by the iteration is exploited. By combining the nonlocal similarity constraints with total variation regularization, an iterative regularized variational model is proposed, in which the nonlocal weight depends on local structure of patches. An effective algorithm is...
By combining fractional differential operator which can enhance image texture information with variational partial differential equation and then applying to image denoising, a denoising model based on fractional partial differential operator is put forward. The model can not only better suppress noise of the image, but also better preserve detailed texture information. However, the order of the fractional...
Non-local means provides a very powerful framework to denoise digital images. Nevertheless, there are several influential parameters on this methodology that are data-dependent and difficult to tune. This paper presents an adaptive image denoising algorithm that uses the non-local means in conjunction with the turbulent particle swarm optimization (i.e. TPSO) which based on a no-reference metric Q...
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%).
A novel enhanced switching median filter incorporating an effective impulse noise detection method called neighborhood based layer discriminative noise detection (NBLDND) is proposed in this paper for de-noising extremely corrupted images. The neighborhood of a pixel is classified into pepper corrupted lower class (PCLC), uncorrupted middle class(UCMC) and salt corrupted higher class (SCHC). The center...
This paper attempts to undertake the study of two types of noise such as Salt and Pepper (SPN), Speckle (SPKN). Different noise densities have been removed by using four types of filters as meidan filter, Lee filter, Kuan filter, Frost filter, and Wavelet based Bivariate Shrinkage function. Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due...
A 2D signal is often used as an image signal in the application of a digital image, which could produce noise in the processing of image acquisition. It is too rough of a traditional method to be used for noise suppression; regarding noise suppression, it will also lose part of the original signal so that makes the image blurred. This paper introduces image reduction based on the wavelet analysis...
This paper proposes a new noise filtering method inspired by Bilateral filter (BF) and non-local means (NLM) filter. The main idea here is to perform the BF in a multidimensional patch-space using an anisotropic kernel. The filtered multidimensional signal is then transformed back onto the image spatial domain to yield the desired enhanced image. The proposed method is compared to the state-of-art...
This paper introduces a recognition method on “Many Different Kinds of Automotive Style on Robot Painting Line Recognition System”. This paper mainly uses the dynamically silhouetted image to confirm the outline of the real automotive style's motional sequence image, then uses the median filtering to eliminate a part of noise, then uses the image erosion technology to eliminate the structure noise...
Wavelet-based denoising has comprehensive functionalities including feature extraction and low-pass filtering, while keeping characteristics such as low entropy, multi-resolution, irrelevance, etc. Wavelet-based denoising methods have been successfully applied for image processing in varieties. However, one of the main factors degrading underwater imaging is the backward scattered light, which performs...
Medical applications of ultrasound imaging have expanded enormously over the last two decades. De-noising is challenging issues for better medical interpretation and diagnosis on high volume of data sets in echocardiography. In this paper, manifold learning algorithm is applied on 2-D echocardiography images to discover the relationship between the frames of consecutive cycles of the heart motion...
In this paper, we propose a content-aware noise reduction (NR) method for the Digital TV, which preserves image textures and edges. In the proposed method, noise is first reduced by a temporal recursive NR filter and then further suppressed by a spatial NR filter. To preserve the image texture, the saliency map which accurately represents edges in a noisy image is employed in the spatial filter. Experimental...
The mean-shift (MS) algorithm is applied for reducing speckle noise and segmenting synthetic aperture radar (SAR) images. Two coastal images acquired by Envisat's advanced SAR (ASAR) [European Space Agency (ESA)] are used. Studies of the MS parameters are carried out according to the desired product: a speckle filtered image where textures and edges are preserved, or a segmented image, where land...
In this paper, a novel approach is proposed for unsupervised change detection of multitemporal remote sensing images. The proposed method is able to produce the change detection result on the difference image without a priori assumptions .Firstly, the difference image which is acquired from multitemporal images. Mean shift algorithm is used to reduce noise of difference image and fake change. Then...
Thyroid diseases, in general, and in particular thyroid cancer can be detected by infrared built-in image processing. A good spatial resolution is needed to assess the specific patterns, but small signal-to-noise ratio and low contrast makes much difficult to achieve infrared image denoising and edge enhancement. This paper proposes an improved method of anisotropic diffusion filtering which reduces...
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