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The algorithm based on stack to remove the small area noises in binary microalgae image is presented in this paper. The denoising of image is accomplished by using this algorithm through scanning image one time. The new algorithm not only preserves signal's original features, but also has stronger ability to remove noise. The numerical experiments have shown that the algorithm is very effective in...
A new method for removal of wide density salt and pepper noise in image is proposed. Every pixel in a noise corrupted image is classified to which it is a noise pixel or noise-free pixel. A noise pixel should be modified by a filter. On the contrary, noise-free pixel is kept unchanged. If noise-free pixel is detected as noise pixel wrongly, the restored image will be blurred. The difference between...
In this paper we are interested in exploiting self-similarity information for discriminative image denoising. Towards this goal, we propose a simple yet powerful denoising method based on transductive Gaussian processes, which introduces self-similarity in the prediction stage. Our approach allows to build a rich similarity measure by learning hyper parameters defining multi-kernel combinations. We...
To improve comprehensive performance of denoising range images, an impulsive noise (IN) denoising method with variable windows is proposed in this paper. Founded on several discriminant criteria, the principles of dropout IN detection and outlier IN detection are provided. Subsequently, a nearest non-IN neighbors searching process and an Index Distance Weighted Mean filter is combined for IN denoising...
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...
Non-local means (NLM) noise reduction is effective yet computationally intensive, since each output pixel is a weighted average of all input pixels. Most implementations therefore restrict the scope of the average to a smaller neighborhood around each pixel, which limits the method's full potential. Here we propose to apply NLM to reduce the noise in fluorescence microscopy image sequences of the...
Removing noise from the original signal is still a challenging problem for researchers. Despite the complexity of the recently proposed methods, most of the algorithms have not yet attained a pleasing level of applicability. This paper presents a review of some significant work in the area of image denoising. After a brief introduction, some of the popular approaches are categorized into different...
This paper proposes a high accuracy and fast image restoration approach to restore a sequence of atmospheric turbulence degraded frames of a remote object or scene. A coarse-to-fine optical flow technique is employed to estimate the dense motion fields of the frames against a reference frame. The First Register Then Average And Subtract (FRTAAS) method is used to correct the geometric distortions...
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...
It is seen that during image acquisition, storage, retrieval or transmission, images get degraded due to presence of noise. With different varieties of noise and its extent, de-noising becomes challenging. Traditionally, a host of techniques have considered spatial, statistical and multiple domain approaches for de-noising. Yet, the scope always exist for exploring innovative means of performing de-noising...
This paper proposes noise suppression using adaptive bilateral filtering especially for Gaussian and speckle noise. This filter is used for the sharpness enhancement of degraded images and performs well for Gaussian noise and speckle noise reduction. Bilateral filtering is a non-linear technique which can smudge the image while preserving strong edges. It is a noniterative image denoising method which...
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...
We have recently proposed a Sequential Generalization of K-means (SGK) to train dictionary for sparse representation. SGK's training performance is as effective as the standard dictionary training algorithm K-SVD, alongside it has a simpler implementation to its advantage. In this piece of work, through the problem of image denoising, we are making a fair comparison between the usability of SGK and...
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...
In this paper, a kind of method for image denoising is proposed which is based on Pulse Coupled Neural Network (PCNN) and conjugate gradient method. Firstly, we compress and add noise on a high resolution image. Then, we remove noise based on PCNN within 5 neighborhoods and 3 neighborhoods. Finally, we use adaptive median filter to modify the image pixels and use the matrix conjugate gradient method...
Image de-noising is an important step to improve the AOI (automated optical inspection) image quality of automobile work piece. After analyzing the theory of wavelet transform and the characteristics of traditional soft and hard wavelet threshold de-noising methods, an improved threshold de-noising method was proposed. The new method overcomes the discontinuous in hard threshold de-noising method...
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...
In order to remove the noise of sonar image more effectively, the adaptive over complete dictionary based on K-SVD algorithm is carried out in this paper. Given a set of training signals from noisy image, the predefined dictionary is trained so that the new dictionary leads to the best sparse representation for sonar image, but not for the noise. Experiments are provided to demonstrate the performance...
During acquisition of an image, from its source, noise becomes integral part of it, which is very difficult to remove. Various algorithms have been used in past to denoise images. Image denoising still has scope for improvement. In this paper we present a new image denoising algorithm based on combined effect of wavelet transform and median filtering. The algorithm removes most of the noisy part from...
In this paper, we propose the non-local method for image de-noising via adaptive directional lifting-based discrete wavelet transform (ADL) and quantization. The non-local methods such as non-local means are interested in image denoising based on the self-similarity. They search similar blocks and estimate the original value. The proposed method doesn't search but generates new similar blocks by ADL...
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