Автоматичне визначення рiвня гаусового шуму на цифрових зображеннях методом видiлених областей

Розроблено метод автоматичного визначення рівня шуму на цифрових зображеннях, а саме середнього квадратичного відхилення гаусового шуму. Рівень шуму обчислюється за середнім квадратичним відхиленням гістограми для виділеної області зображення, на якій наявний, в основному, шум. Запропонований метод програмно реалізовано в системі MATLAB. Оброблення тестових зображень з використанням запропонованого методу дало змогу отримати меншу похибку обчислення рівня шуму ніж іншими сучасними методами.Разработан метод автоматического определения уровня шума на цифровых изображениях, а именно среднего квадратичного отклонения гауссовского шума. Уровень шума вычисляется через среднее квадратичное отклонение гистограммы для выделенной области изображения, на которой имеется, в основном, шум. Предложенный метод программно реализован в системе MATLAB. При обработке тестовых изображений предложенным методом получена меньшая погрешность вычисления уровня шума, чем при использовании других современных методов.The purpose of the article is to develop an automatic method of Gaussian noise level determination in digital images, which uses the selection of image region based on its lowfrequency filtering and performs calculation of noise level by analyzing of histograms of the selected region. The article is aimed at software implementation of the elaborated method in the MATLAB system and estimation of its accuracy by processing the collection of test images

Development of an Advanced Technique for Historical Document Preservation

In this paper, technique used for historical document preservation is explored. In this paper a noise estimation technique is applied to know noise standard deviation. We first estimate or detect level of noise present in noisy images by selecting weak textured patches in image on the basis of gradient matrix and its statistical properties, then eliminate that noise through non local means(NLM) denoising technique that will use estimated noise level as filtering parameter for eliminating noise from the image. This technique is based on weighted average of the similar pixels in historical image. Non local means techniques removes noise from images without taking care of noise level ,it is mandatory to take care of noise level for best preserving Historical document images

Identifikasi Karakteristik Citra Berdasarkan pada Nilai Entropi dan Kontras

Abstract Determining the object boundaries in an image is a necessary process, to identify the boundaries of an object with other objects as well as to define an object in the image. The acquired image is not always in good condition, on the other hand there is a lot of noise and blur. Various edge detection methods have been developed by providing noise parameters to reduce noise, and adding a blur parameter but because these parameters apply to the entire image, but lossing some edges due to these parameters. This study aims to identify the characteristics of the image region, whether the region condition is noise, blurry or otherwise sharp (clear). The step is done by dividing the four regions from the image size, then calculating the entropy value and contrast value of each formed region. The test results show that changes in region size can produce different characteristics, this is indicated by entropy and contrast values ​​of each formed region. Thus it can be concluded that entropy and contrast can be used as a way to identify image characteristics, and dividing the image into regions provides more detailed image characteristics. &nbsp

Enhancing Patch-Based Methods with Inter-frame Connectivity for Denoising Multi-frame Images

The 3D block matching (BM3D) method is among the state-of-art methods for denoising images corrupted with additive white Gaussian noise. With the help of a novel inter-frame connectivity strategy, we propose an extension of the BM3D method for the scenario where we have multiple images of the same scene. Our proposed extension outperforms all the existing trivial and non-trivial extensions of patch-based denoising methods for multi-frame images. We can achieve a quality difference of as high as 28% over the next best method without using any additional parameters. Our method can also be easily generalised to other similar existing patch-based methods

Image Denoising by using Modified SGHP Algorithm

In real time applications, image denoising is a predominant task. This task makes adequate preparation for images looks prominent. But there are several denoising algorithms and every algorithm has its own distinctive attribute based upon different natural images. In this paper, we proposed a perspective that is modified parameter in S-Gradient Histogram Preservation denoising method. S-Gradient Histogram Preservation is a method to compute the structure gradient histogram from the noisy observation by taking different noise standard deviations of different images. The performance of this method is enumerated in terms of peak signal to noise ratio and structural similarity index of a particular image. In this paper, mainly focus on peak signal to noise ratio, structural similarity index, noise estimation and a measure of structure gradient histogram of a given image

JPG, PNG and BMP image compression using discrete cosine transform

This paper proposes image compression using discrete cosine transform (DCT) for the format of joint photographic expert groups (JPEG) or JPG, portable network graphic (PNG) and bitmap (BMP). These three extensions are the most popular types used in current image processing storage. The purpose of image compression is to produce lower memory usage or to reduce memory file. This process removes redundant information of each pixel. The challenge for image compression process is to maintain the quality of images after the compression process. Hence, this article utilizes the DCT technique to sustain the image quality and at the same time reduces the image storage size. The effectiveness of the DCT technique has been reasonable over some real images and the implementation of the technique has been compared with different types of image extensions. Matlab software is an important platform for this project in order to write a program and perform the progress of project phase by phase to achieve the expected results. Based on the tested images, the DCT technique in image compression is capable to reduce the image storage memory in average about 50% of each image tested

Mean of Median Absolute Derivation Technique for Speckle Noise Variance Estimation in Computerised Tomography Images

The accurate estimation of noise variance in an image is the first important stage in image filtering using adaptive filters. In this paper, a new technique for the estimation of speckle noise present in Computerised Tomography (CT) lung image was developed. The development of mean of median absolute derivation technique based on the estimated mean of speckle noise present in CT images is presented. From the result of the simulations, the new technique gave a reasonably accurate estimate of variance of speckle noise present in CT Images. Ten samples of 85x73 CT images corrupted by speckle noise level ranging from 10% to 30% where used as test images. Also, the new technique gave the lowest average speckle noise variance estimation error of 2.53% compared to 12.53% for the Median of Median Absolute Derivative Technique, 18.18% for the Transfer function technique and 37.14% for the Mode of Variance Technique. The simulation software used in the paper is Matrix Laboratory (MATLAB2012).http://dx.doi.org/10.4314/njt.v34i2.2

Image and Texture Independent Deep Learning Noise Estimation using Multiple Frames

In this study, a novel multiple-frame based image and texture independent convolutional Neural Network (CNN) noise estimator is introduced. The estimator works

Mixed Noise Removal by Processing of Patches

Sonar images are degraded by mixed noise which has an adverse impact on detection and classification of underwater objects. Existing denoising methods of sonar images remove either additive noise or multiplicative noise. In this study, the mixed noise in sonar images, the additive Gaussian noise and the multiplicative speckle effect are handled by the data adaptive methods. A patch based denoising is applied in two phases to remove the additive Gaussian and multiplicative speckle noises. In the first phase, the adaptive processing of local patches is used to remove the additive Gaussian noise by exploiting the sonar image local sparsity. The PCA and SVD methods are used for denoising the noisy image patches and blocks of patches. In the second phase, the weighted maximum likelihood denoising of the nonlocal patches reduces the speckle effect by exploiting the non-local similarity in a probability distribution. Experiments on side scan sonar images are conducted and the results show the importance of removing both the additive and multiplicative components from the sonar images