Homogeneous Image Regions Research Articles

GAP LINES REDUCTION FOR ENHACED TEMPORAL LANDSAT ETM+ IMAGERY USING MORPHOLOGICAL OPRATIONS

The failure of the Scanning Line Corrector of ETM+2004 of Landsat7 in imposed systematic data gap lines on retrieved the correct satellite images and rejected the ability to provide continuous information in different scientific fields. While a many processing algorithms proposed to fill out this gap lines, utmost of these algorithms are just only valid through the homogeneous image regions. When there are applied to process the heterogeneous ETM+landsat7 images, returning image pixels and image features can’t become correct and precise. In this study a gap filling algorithm has been proposed that is based on the morphologic operations of image. The operations used here are combination of the processes, dilation and erosion. The algorithm employs opening, closing morphologic operations of gap image methods to identify the properties and size the separated gab lines image for ETM+landsat7 images. Each band images has been passed through milt-image processing stages. The images that output of all stages are eight corrected images. Enhanced images are very clear and the gab lines completely disappear from all image bands. The algorithm succeeded in correction and filling gab lines for homogeneous and heterogeneous image areas.

Stereo Image Matching Using Adaptive Morphological Correlation.

A stereo matching method based on adaptive morphological correlation is presented. The point correspondences of an input pair of stereo images are determined by matching locally adaptive image windows using the suggested morphological correlation that is optimal with respect to an introduced binary dissimilarity-to-matching ratio criterion. The proposed method is capable of determining the point correspondences in homogeneous image regions and at the edges of scene objects of input stereo images with high accuracy. Furthermore, unknown correspondences of occluded and not matched points in the scene can be successfully recovered using a simple proposed post-processing. The performance of the proposed method is exhaustively tested for stereo matching in terms of objective measures using known database images. In addition, the obtained results are discussed and compared with those of two similar state-of-the-art methods.

SAR Image Despeckling Algorithm Using Non-Local Means with Adaptive Filtering Strength

A new Non-Local Means (NLM) despeckling algorithm (AFS-NLM) with Adaptive Filtering Strength (AFS) is proposed to improve the performance of reducing multiplicative speckle and preserving the edges in SAR images. A modified Kuan filtering coefficient which can better characterize the homogeneous and edge regions of SAR image is formed by using the local mean and variance calculated in the Frost filtered image to improve the estimation of SAR image scene parameters. An improved NLM which adapts to the multiplicative noise characteristics is constructed by the new similarity measurement parameter estimated by the local mean ratio and the new adaptive decay factor estimated by the improved Kuan filtering coefficient. A new weighted filtering model which can automatically adjust the filtering strength is formed. In the new model, the improved NLM filters controlled by the skew smoothing parameters and the skew edge protection parameters are used to replace the local average value of pixels and the gray value of pixels in the classic Kuan filter model as weighting items, and the adaptive adjustment factor constructed by the improved Kuan filter coefficient is used to weight the two items. Experimental results and comparisons with several advanced despeckling algorithms in recent years show that the proposed algorithm has better speckle suppression and edge preservation performance.

SAR image despeckling method based on improved Frost filtering

In this paper, a novel despeckling algorithm for synthetic aperture radar (SAR) images based on improved Frost filtering is proposed. A reconstructed decay factor which can better characterize the homogeneous and edge regions of SAR image is added into the Frost filter model by using the Lee filter coefficient to implement adaptively controlling the degree of filtering in different regions. The three-stage filtering strategy is performed for effective despeckling and edge preservation. The SAR image is pre-filtered by using the Frost filtering of the small window to obtain local statistics, and then the original SAR image is refine filtered twice utilizing these local statistical parameters. The two values are, respectively, biased toward smoothing and edge preservation by adjusting the variable constant and the window scales. Finally, the final filtering result is obtained by weighting the two filtering results with the reconstructed decay factor we proposed. The experiments demonstrate that compared with advanced approaches, the proposed method improves both despeckling and edge preservation ability.

A DE-ANN Inspired Skin Cancer Detection Approach Using Fuzzy C-Means Clustering

As per recent developments in medical science, the skin cancer is considered as one of the common type disease in human body. Although the presence of melanoma is viewed as a form of cancer, it is challenging to predict it. If melanoma or other skin diseases are identified in the early stages, prognosis can then be successfully achieved to cure them. For this, medical imaging science plays an essential role in detecting such types of skin lesions quickly and accurately. The application of our approaches is to improve skin cancer detection accuracy in medical imaging and further, can be automated using electronic devices such as mobile phones etc. In the proposed paper, an improved strategy to detect three type of skin cancers in early stages are suggested. The considered input is a skin lesion image which by using the proposed method, the system would classify it into cancerous or non-cancerous type of skin. The image segmentation is implemented using fuzzy C-means clustering to separate homogeneous image regions. The preprocessing is done using different filters to enhance the image attributes while the other features are assessed by implementing rgb color-space, Local Binary Pattern (LBP) and GLCM methods altogether. Further, for classification, artificial neural network (ANN) is trained using differential evolution (DE) algorithm. Various features are accurately estimated to achieve better results using skin cancer image datasets namely HAM10000 and PH2. The novelty of the work suggests that DE-ANN is best compared among other traditional classifiers in terms of detection accuracy as discussed in result section of this paper. The simulated result shows that the proposed technique effectually detects skin cancer and produces an accuracy of 97.4%. The results are highly accurate compare to other traditional approaches in the same domain.

Parameter Estimation of Signal-Dependent Random Noise in CMOS/CCD Image Sensor Based on Numerical Characteristic of Mixed Poisson Noise Samples.

Parameter estimation of Poisson-Gaussian signal-dependent random noise in the complementary metal-oxide semiconductor/charge-coupled device image sensor is a significant step in eliminating noise. The existing estimation algorithms, which are based on finding homogeneous regions, acquire the pair of the variances of noise and the intensities of every homogeneous region to fit the linear or piecewise linear curve and ascertain the noise parameters accordingly. In contrast to the existing algorithms, in this study, the Poisson noise samples of all homogeneous regions in every block image are pieced together to constitute a larger sample following the mixed Poisson noise distribution; then, the mean and variance of the mixed Poisson noise sample are deduced. Next, the mapping function among the noise parameters to be estimated—variance of Poisson-Gaussian noise and that of Gaussian noise corresponding to the stitched region in every block image—is constructed. Finally, the unbiased estimations of noise parameters are calculated from the mapping functions of all the image blocks. The experimental results confirm that the proposed method can obtain lower mean absolute error values of estimated noise parameters than the conventional ones.

Speckle Suppression by Weighted Euclidean Distance Anisotropic Diffusion

To better reduce image speckle noise while also maintaining edge information in synthetic aperture radar (SAR) images, we propose a novel anisotropic diffusion algorithm using weighted Euclidean distance (WEDAD). Presented here is a modified speckle reducing anisotropic diffusion (SRAD) method, which constructs a new edge detection operator using weighted Euclidean distances. The new edge detection operator can adaptively distinguish between homogenous and heterogeneous image regions, effectively generate anisotropic diffusion coefficients for each image pixel, and filter each pixel at different scales. Additionally, the effects of two different weighting methods (Gaussian weighting and non-linear weighting) of de-noising were analyzed. The effect of different adjustment coefficient settings on speckle suppression was also explored. A series of experiments were conducted using an added noise image, GF-3 SAR image, and YG-29 SAR image. The experimental results demonstrate that the proposed method can not only significantly suppress speckle, thus improving the visual effects, but also better preserve the edge information of images.

An Efficient Curvelet Framework for Denoising Images

Wiener filter suppresses noise efficiently. However, it makes the out image blurred. Curvelet preserves the edges of natural images perfectly, but, it produces visual distortion artifacts and fuzzy edges to the restored image, especially in homogeneous regions of images. In this paper, a new image denoising framework based on Curvelet transform and wiener filter is proposed, which can stop noise better than these methods. The performance of introduced scheme is evaluated in terms of two important denoising criteria, PSNR and SSIM on standard test images in different noise levels. Three famous thresholding ‘soft’, ‘semisoft’ and ‘hard’ are applied to noisy images and results are fused by the wavelet transform to form restore images. Our framework outperforms the curvelet transform denoising by %6.3 in terms of PSNR and %5.9 in terms of SSIM for ‘Lena’ image. The visual outputs show that false artifacts, parasite lines and the blurring degree of output images, are reduced significantly. The obtained results reveal the superiority of our framework over recent reported methods.

Simultaneously Sparse and Low-Rank Abundance Matrix Estimation for Hyperspectral Image Unmixing

In a plethora of applications dealing with inverse problems, e.g. in image processing, social networks, compressive sensing, biological data processing etc., the signal of interest is known to be structured in several ways at the same time. This premise has recently guided the research to the innovative and meaningful idea of imposing multiple constraints on the parameters involved in the problem under study. For instance, when dealing with problems whose parameters form sparse and low-rank matrices, the adoption of suitably combined constraints imposing sparsity and low-rankness, is expected to yield substantially enhanced estimation results. In this paper, we address the spectral unmixing problem in hyperspectral images. Specifically, two novel unmixing algorithms are introduced, in an attempt to exploit both spatial correlation and sparse representation of pixels lying in homogeneous regions of hyperspectral images. To this end, a novel convex mixed penalty term is first defined consisting of the sum of the weighted $\ell_1$ and the weighted nuclear norm of the abundance matrix corresponding to a small area of the image determined by a sliding square window. This penalty term is then used to regularize a conventional quadratic cost function and impose simultaneously sparsity and row-rankness on the abundance matrix. The resulting regularized cost function is minimized by a) an incremental proximal sparse and low-rank unmixing algorithm and b) an algorithm based on the alternating minimization method of multipliers (ADMM). The effectiveness of the proposed algorithms is illustrated in experiments conducted both on simulated and real data.

The segmentation of 3D images using the random walking technique on a randomly created image adjacency graph.

This paper considers the problem of image segmentation using the random walker algorithm. In the case of 3D images, the method uses an extreme amount of memory and time resources. These are required in order to represent the corresponding enormous image graph and to solve the resulting sparse linear system. Having in mind these limitations, this paper proposes techniques for the optimization of the random walker approach. The optimization is obtained by processing supervoxels representing homogeneous image regions rather than single voxels. A fast and efficient method for supervoxel determination is introduced. A method for the creation of an image adjacency graph from an irregular grid of supervoxels is also proposed. The results of applying the introduced approach to segmentation of 3D CT data sets are presented and compared with the results of the original random walker approach and other state-of-the-art methods. The accuracy and the computational overhead is regarded in the comparison. The analysis of results shows that the modified method can be successfully applied for the segmentation of volumetric images and provides results in a reasonable time without a significant loss in the image segmentation accuracy. It also outperforms the state-of-the-art methods considered in the comparison.

Detection of quiescent cardiac phases in echocardiography data using nonlinear filtering and boundary detection techniques.

We describe an algorithm to detect cardiac quiescence within a heartbeat using nonlinear filtering and boundary detection techniques in echocardiography images. The motivation for detection of these quiescent phases is to provide improved cardiac gating to obtain motion-artifact-free images of the heart at cardiac computed tomography (CT). Currently, cardiac gating is provided through electrocardiography (ECG), which does not provide information about the instantaneous mechanical state of the heart. Our goal is to test if information about the actual mechanical motion of the heart obtained from B-mode echocardiographic data could potentially be used for gating purposes. The nonlinear filtering algorithm presented involves anisotropic diffusion to smoothen the homogeneous regions of the B-mode images while preserving image edges that represent myocardial boundaries. Following this, we detect the boundary of a particular region of interest (ROI) using a thresholding step. The positional changes of this ROI are then observed for quiescent phases over multiple cardiac cycles using the ECG's R-R interval. In a pilot study, seven subjects were imaged in the apical, four-chamber view, and quiescence of the interventricular septum was primarily observed in the diastolic region of the ECG signal. However, the position and length of quiescence vary across multiple heartbeats for the same individual and for different individuals as well. The center of quiescence for the seven patients ranged from 51 to 84% and did not show a trend with heart rates, which ranged from 54 to 83 beats per minute. The gating intervals based on such analysis of echocardiographic signals could potentially optimize cardiac CT gating.

Multiplicative noise removal combining a total variation regularizer and a nonconvex regularizer

A novel variational model for removing multiplicative noise is proposed in this paper. In the model, a novel regularization term is elaborately designed which is inherently equivalent to a combination of the classical total variation regularizer and a nonconvex regularizer. The proposed regularization term, on the one hand, can better remove the noise in homogeneous regions of a noisy image and, on the other hand, can preserve edge details of the image during the denoising process. In order to solve the model efficiently, we design an alternating iteration process in which two coupling minimization problems are solved. For each of the two minimization problems, the existence and uniqueness of their solutions are proved under some necessary assumptions. Numerical results are reported to demonstrate the effectiveness of the proposed regularization term for multiplicative noise removal.

Automatic Image Annotation Using Fuzzy Cross-Media Relevance Models

In this paper, the authors propose a novel automatic image annotation approach which relies on two main components: (i) identification of homogenous image regions, which share the same semantics using fuzzy clustering algorithm, and (ii) membership-based cross media relevance model to learn the association between

EVALUATION AND ENHANCEMENT OF CONTRACT IN DIGITAL IMAGES

The optical imaging systems, do not give a perfect and ideal image. But mostly give images with limited resolutions i.e. give images with blurr edges. This blurring attributed to two main reasons. The diffraction of light and Aberrations of the optical imaging system. The most important information and details in the image that contained in image edges. The fidelity criteria are evaluate the quality, depending on the whole image plane or in homogenous image regions. So that we devoted in this study to evaluate image quality depending on compute the image contrast in edge regions, and introduce robust quantitative measures to determine image quality, then estimate the efficiency of the various techniques in image processing applications. In this study we suggested new techniques to calculate image contrast (visibility) and studying it as a function of number of smoothing iterations from using mean filter and a function of gray level resolution. We only study the contrast in edge regions where we used Soble operator to find image edges. The suggested techniques are:- 1- Technique of measure contrast based on calculates the mean of dark points and the mean of bright points in the image edge regions. 2- The technique compute the triple contrast of the image where the first: global contrast (C0) in image edge compute as in direct technique, the second local edge contrast compute for the largest image edge points, while the third is the local contrast for the smallest image edge points. The results give high agreement among the various suggested methodsin determines image contrast. As we can theoretical guested, that the contrast reduced with reducing spatial and image gray level resolutions.Here can be say that we get a robust quantitative measures that could be used to estimate the efficiency of the image processing techniques, based on determine image contrast and find the amount of variation in contrast that causes from the processing steps.

Blind noisy image quality assessment using block homogeneity

Blind noisy image quality assessment aims to evaluate the quality of the degraded noisy image without the need for the ground truth image. To tackle this challenge, this paper proposes an image quality assessment approach using block homogeneity. First, motivated by the local smoothness characteristic of the image, a block-based homogeneity measure is proposed to estimate the statistics (e.g., variance) of the noise incurred in the image, based on adaptively selected homogeneous image regions. Second, an image quality assessment approach is proposed by exploitie above-mentioned estimated noise variance, along with the visual masking effect of the human visual system. Experimental results are provided to demonstrate that the proposed image noise estimation approach yields superior accuracy and stability performance to that of conventional approaches, and the proposed image quality assessment approach achieves consistent performance to that of human subjective evaluation.

Agriculture satellite image segmentation using a modified artificial Hopfield neural network

Beekeeping plays an important role in increasing and diversifying the incomes of many rural communities in Kingdom of Saudi Arabia. However, despite the region’s relatively good rainfall, which results in better forage conditions, bees and beekeepers are greatly affected by seasonal shortages of bee forage. Because of these shortages, beekeepers must continually move their colonies in search of better forage. The aim of this paper is to determine the actual bee forage areas with specific characteristics like population density, ecological distribution, flowering phenology based on color satellite image segmentation. Satellite images are currently used as an efficient tool for agricultural management and monitoring. It is also one of the most difficult image segmentation problems due to factors like environmental conditions, poor resolution and poor illumination. Pixel clustering is a popular way of determining the homogeneous image regions, corresponding to the different land cover types, based on their spectral properties. In this paper Hopfield neural network (HNN) is introduced as Pixel clustering based segmentation method for agriculture satellite images.

A multispectral image segmentation approach for object-based image classification of high resolution satellite imagery

Image segmentation has been recognized as an essential process that performs an object-based rather than a pixel-based classification of high-resolution satellite imagery. This paper presents an efficient image segmentation method that considers the spatial and spectral information of high-resolution pan-sharpened imagery. First, we conduct multispectral nonlinear edge preserving smoothing and extract the multispectral edge, which is used as valuable information for seed selection and image segmentation. The initial seeds are automatically selected using the proposed edge variation-based seed selection method, which uses the obtained multispectral edge in a local region. After automatic selection of significant seeds, image segmentation is achieved by applying the modified seeded region growing procedure, which integrates the multispectral and gradient information existing in the image to provide homogenous image regions with accurate and closed boundaries. Experimental results on two multispectral satellite images are given to show that the proposed approach has capability superiority to the previous segmentation techniques on visual evaluation and quantitative comparative assessment.

Colour image smoothing through a soft-switching mechanism using a graph model

In this study, the authors propose a soft-switching filter to improve the performance of recent colour image smoothing filters when processing homogeneous image regions. The authors use a recent filter mixed with the classical arithmetic mean filter (AMF). The recent method is used to process image pixels close to edges, texture and details and the AMF is only used to process homogeneous regions. To this end, the authors propose a method based on the graph theory to distinguish image details and homogeneous regions and to perform a soft switching between the two filters. Experimental results show that the proposed method provides improved results which supports the appropriateness of the graph theory-based method and suggests that the same structure can be used to improve the performance of other non-linear colour image smoothing methods.

PERBANDINGAN METODE-METODE EDGE DETECTION UNTUK PROSES SEGMENTASI CITRA DIGITAL

Edge detection (Edge detection) is executed to detect the operating margins (edges) that restricts two homogeneous image regions with different brightness levels (Pitas, 1993). A digital image edge detection is the process of looking for differences in the intensity of the states boundaries of an object (sub-images) in the entire digital image in question. Purpose of edge detection is to improve the appearance of the boundary line of an area or object in the image. The process of image edge detection is done by looking for the locations of the intensity of the pixels that discontinue with the intensity of the pixels adjacent (neighboring / neighborhood).There are several methods such as object edge detection operators are known, such as (1) Sobel, (2) Prewitt, (3) Robert, (4) Laplacian of Gaussian, (5) and Canny (6) Kiresh. Each operator has its own advantages and disadvantages in performing edge detection. By doing a combination or in the development of these operators allow to gain a better edge detection (obviously).One of the contributions of this study for science and technology is the creation of a software application that can be used as a simulation tool using edge detection operators in digital image and its various combinations in order to gain a better edge detection (obviously).Keywords: Edge detection, detection operator

SNR Evaluation of the RapidEye Space-borne Cameras

Summary: After launch and continuous radiation exposure, space-borne cameras are constantly changing. Therefore permanent technical specification and evaluation of the sensor in space plays an important role in the remote sensing community. There are a variety of evaluation criteria, which are all based on the essential camera parameters – the spatial resolution, point spread function (PSF) and noise. Noise estimation is a challenging task for characterization of remote sensing systems in space. The in-flight measurement of noise will often be done with artificial test sites. If these test sites are not suffi-ciently available, homogeneous image regions (desert, snow, water surfaces) are often used. The al-bedo of these objects, however, lies normally outside the specified albedo range of remote sensing systems focused on the Earth's surface. The only possibility to determine the noise after the satellite launch within the normal operational albedo range is to use normal surface objects within the oper-ationally acquired imagery. As these objects have to be homogeneous, one needs methods that can detect the smallest homogeneous areas in the image to evaluate noise. In this paper an approach for determining the signal to noise ratio (SNR) with data from natural tar-gets is presented. In experiments, the results demonstrate that the described method performs well and results are comparable to the standard methods used to determine SNR.