Showing papers on "Edge enhancement published in 2014"

An augmented-reality edge enhancement application for Google Glass.

Abstract: Most patients with advanced age-related macular degeneration (AMD) experience reduced visual acuity (VA) and contrast sensitivity (CS) because they have to rely on the residual non-foveal retina to inspect targets of interest. Although patients’ peri-peripheral vision is sufficient to recognize the gist of scenes,1 their quality of life is significantly affected by the impairment.2–4 The reduced visual function has a large impact on emotional well-being,5 and social engagement,6 especially due to its effect on tasks such as face recognition,7, 8 which require the ability to discriminate fine details or small contrast differences. The low vision enhancement system (LVES) was the first such commercial system for distance use, utilizing an opaque head mounted display (HMD). It improved visual acuity and contrast sensitivity by converting the camera image into high contrast-magnified video.9 Later, video-based contrast enhancement and zoom-controlling HMD devices were commercialized, as the Jordy (Enhanced vision, CA, USA) and the SightMate (Vuzix, NY, USA). All these devices use full virtual vision HMDs that block the wearer’s natural field of vision. An alternative approach, augmented reality (AR) using optical see-through HMDs, was proposed by Peli for various vision conditions,10 for example, employing wideband image enhancement to enhance the visibility of edges of the scene, as an alternative to magnification.11, 12 Luo and Peli developed a hybrid see-through/opaque HMD device that superimposed a scene edge-view over the wearer’s natural view in see-through mode, but the display could be made opaque to display only a magnified scene edge-view.13, 14 Using desktop displays, it has been shown that adding high contrast edges to static images,15 or videos16 is preferred by patients with AMD,15, 16 and improved visual search performance of elderly with simulated central visual loss.15, 17 Precise alignment of the augmented edges with the see-through scene is necessary, but difficult to achieve in the HMD application, because the camera and the display are usually shifted relative to each other. The parallax produced by that shift varies with distance to the objects of regard. An on-axis HMD-camera configuration was attempted and achieved excellent edge alignment, but the optical system aligning the camera and display reduced display brightness to such an extent that the augmented edges were too dim to provide a significant boost in visibility.18 Google Inc. (Mountain View, CA) has recently introduced a novel HMD system, Google Glass, which is intended for an interactive personal communication device. Glass provides a hardware and software development platform that can be applied to vision enhancement for patients with impaired vision. It features a wide-field high-resolution camera, a small optical see-through display positioned above the line of sight, a rechargeable battery, and enough computing power for image processing. The Android-based operating system, which is open for custom app development, supports OpenGL GPU computations and camera access. Here, we report a preliminary exploration of the utility of Google Glass as a visual aid for patients with AMD by developing an app that provides edge enhancement as an augmented view. The real view of the environment through the Glass display is multiplexed with a scaled cartoonized outline of the view, captured by the camera. The user sees enhanced contrast at the location of edges in the real-world. We show how the spatial alignment problem between the augmented edges and the real view can be addressed to provide such an augmented view. We also describe how we generate and display augmented edge information, where the edge enhancement method and range of enhancement is user selectable, because optimal settings of these factors depend on the individual user’s impairment and the immediate task that the user anticipates, as well as the visual and environment and lighting conditions. The user interface for controlling system parameters must be simple, intuitive, and quick.

Quantitative evaluation of a single-distance phase-retrieval method applied on in-line phase-contrast images of a mouse lung.

Abstract: Propagation-based X-ray phase-contrast computed tomography (PBI) has already proven its potential in a great variety of soft-tissue-related applications including lung imaging. However, the strong edge enhancement, caused by the phase effects, often hampers image segmentation and therefore the quantitative analysis of data sets. Here, the benefits of applying single-distance phase retrieval prior to the three-dimensional reconstruction (PhR) are discussed and quantified compared with three-dimensional reconstructions of conventional PBI data sets in terms of contrast-to-noise ratio (CNR) and preservation of image features. The PhR data sets show more than a tenfold higher CNR and only minor blurring of the edges when compared with PBI in a predominately absorption-based set-up. Accordingly, phase retrieval increases the sensitivity and provides more functionality in computed tomography imaging.

A hybrid approach to Image Enhancement using Contrast Stretching on Image Sharpening and the analysis of various cases arising using histogram

Abstract: Image Enhancement is one of the first steps in Image processing. In this technique, the original image is processed so that the resultant image is more suitable than the original for specific applications. Preprocessing an image include removal of noise, edge or boundary enhancement, automatic edge detection, automatic contrast adjustment and segmentation. Image enhancement is a purely subjective processing technique. An image enhancement technique used to process images might be excellent for a person but the same result might not be good enough for another. Image enhancement is a cosmetic procedure i.e. it does not add any extra information to the original image. It merely improves the subjective quality of the images by working with the existing data. This Research paper focuses on Contrast Stretching and Image Sharpening techniques. In this paper, an approach that simultaneously adjusts contrast and enhances boundaries is presented. Histogram has been plotted to verify the result of various cases arising due to implementation of contrast stretching on image sharpening. The edges of the objects in the image are also enhanced by this methodology. Various other edge enhancement techniques are also available like Contrast Stretching on Adaptive Thresholding for enhancing edges of MRI knee images. The proposed methodology shows much better result than the one said above.

Various Image Enhancement Techniques - A Critical Review

Abstract: Image Enhancement is very essential and important technique used in image processing. The role of image enhancement is to improve the content visibility of an image. Images in different fields like medical, satellite images, aerial images and even real life pictures suffer from poor contrast and high noise. It is important to only enhance the contrast and reduce the noise to increase image quality. The enhancement technique differs according to various aspects and they can be broadly classified into two categories: Spatial Domain and Frequency domain based techniques. This paper presents a review of image enhancement processing techniques in spatial domain. Also we have categorized processing methods based representative techniques of Image enhancement. Thus this paper helps to evaluate various image enhancement techniques.

High dynamic range image tone mapping by optimizing tone mapped image quality index

Abstract: An active research topic in recent years is to design tone mapping operators (TMOs) that convert high dynamic range (H-DR) to low dynamic range (LDR) images, so that HDR images can be visualized on standard displays. Nevertheless, most existing work has been done in the absence of a well-established and subject-validated image quality assessment (IQA) model, without which fair comparisons and further improvement are difficult. Recently, a tone mapped image quality index (TMQI) was proposed, which has shown to have good correlation with subjective evaluations of tone mapped images. Here we propose a substantially different approach to design TMO, where instead of using any pre-defined systematic computational structure (such as image transformation or contrast/edge enhancement) for tone mapping, we navigate in the space of all images, searching for the image that optimizes TMQI. The navigation involves an iterative process that alternately improves the structural fidelity and statistical naturalness of the resulting image, which are the two fundamental building blocks in TMQI. Experiments demonstrate the superior performance of the proposed method.

Directional edge enhancement using superposed vortex filter

Abstract: A new filter with azimuthal amplitude variation for directional edge enhancement is demonstrated. The filter can be seen as a superposition of two radial Hilbert filters of opposite topology. Directional edge enhancement property is explained by studying the filter and its transfer functions. Experimental and simulation results are presented and the advantages of this filter in fingerprint contrast enhancement are shown.

Localized image enhancement

Abstract: Image enhancement is a well established field in image processing. The main objective of image enhancement is to increase the perceptual information contained in an image for better representation using some intermediate steps, like, contrast enhancement, debluring, denoising etc. Among them, contrast enhancement is especially important as human eyes are more sensitive to luminance than the chrominance components of an image. Most of the contrast enhancement algorithms proposed till now are global methods. The major drawback of this global approach is that in practical scenarios, the contrast of an image does not deteriorate uniformly and the outputs of the enhancement techniques reach saturation at proper contrast points. That leads to information loss. In fact, to the best of our knowledge, no non-reference perceptual measure of image quality has yet been proposed to measure localized enhancement. We propose a fast algorithm to increase the contrast of an image locally using singular value decomposition (SVD) approach and attempt to define some parameters which can give clues related to the progress of the enhancement process.

Overview on Edge Detection Methods

Abstract: Edge in an image is a contour across which the brightness of the image changes abruptly. Edge detection plays a vital role in image processing. Edge detection is a process that detects the presence and location of edges constituted by sharp changes in intensity of the image. An important property of the edge detection method is its ability to extract the accurate edge line with good orientation. Different edge detectors work better under different conditions. Comparative evaluation of different methods of edge detection makes it easy to decide which edge detection method is appropriate for image segmentation. This paper presents an overview of the published work on edge detection.

Self-adaptive low-illuminance or non-uniform-brightness image enhancement method

Abstract: The invention relates to a self-adaptive low-illuminance or non-uniform-brightness image enhancement method. The method comprises the following steps: 1), preprocessing is performed on a low-illuminance and non-uniform-brightness image, wherein the preprocessing includes brightness preprocessing on the low-illuminance and non-uniform-brightness image, and edge enhancement is performed on the image after brightness preprocessing, so that the preprocessed image is obtained; 2), region segmentation is performed according to the brightness of the preprocessed image, corresponding mapping functions are selected according to the different characteristics of all segmented regions and corresponding self-adaptive brightness enhancement is performed; 3), saturation enhancement processing is performed on the image subjected to self-adaptive brightness enhancement segment by segment through the change characteristics of initial saturation and brightness. According to the invention, the steps are adopted to process the image, therefore, the color saturation of the image is improved, the image is enabled to be bright in color and have a better visual effect. The self-adaptive image enhancement method can be widely popularized in the fields of biomedicine, real-time monitoring, satellite remote sensing and the like.

Total variation based edge enhancement for level set segmentation and asymmetry analysis in breast thermograms.

Abstract: In this work, an attempt has been made to perform asymmetry analysis in breast thermograms using non-linear total variation diffusion filter and reaction diffusion based level set method. Breast images used in this study are obtained from online database of the project PROENG. Initially the images are subjected to total variation (TV) diffusion filter to generate the edge map. Reaction diffusion based level set method is employed to segment the breast tissues using TV edge map as stopping boundary function. Asymmetry analysis is performed on the segmented breast tissues using wavelet based structural texture features. The results show that nonlinear total variation based reaction diffusion level set method could efficiently segment the breast tissues. This method yields high correlation between the segmented output and the ground truth than the conventional level set. Structural texture features extracted from the wavelet coefficients are found to be significant in demarcating normal and abnormal tissues. Hence, it appears that the asymmetry analysis on segmented breast tissues extracted using total variation edge map can be used efficiently to identify the pathological conditions of breast thermograms.

Determination of Image Edge Width by Unsharp Masking

Abstract: A new method is developed for assessing the image edge width based on the unsharp masking approach. A model of the image edge is proposed and a complete solution of the edge width determination problem is obtained. The accuracy of edge determination is analyzed as a function of the length of segments on which profile information is specified and the noise level. An application of the method to assess the quality of an ophthalmological image is reported.

Infrared image enhancement techniques — A review

Abstract: From the very beginning of the concept of image processing, the researchers took the challenge of image enhancement process as an important focus since enhancing an image would result in improvement in the image quality. Image has to be enhanced prior to any mentioned processing. An optimal Enhancement technique should enhance both high quality and low quality images, and should highlight even small details hidden in the image. Infrared image enhancement refines the details immerged in the background and provide a noise free image as output. This paper was aimed to discuss and analyze about various image enhancement techniques and filters that are used to enhance the quality of the given input image.

Implementation of dynamic-range enhancement and super-resolution algorithms for medical image processing

Abstract: Two sophisticated algorithms aimed at `dynamic-range enhancement' and `super-resolution' are implemented dedicatedly for medical image processing. The first is intended for adaptive dynamic-range enhancement, originally developed as a means to enhance digital camera images captured under adverse conditions, such as contrejour and poor ambient lighting, which has been optimized for real-time processing of high-resolution video images, and has scored a design win in a commercial endo-scopic system. The second is targeted for single-frame superresolution, initially developed as a scaler for entertainment consoles and HDTV, which is now under evaluation as a means to provide digital zoom capabilities to medical imaging systems. FPGA implementation results of these procedures are also shown to demonstrate that the devised algorithms can be efficiently put to practical use in medical image processing and is capable of processing high-resolution video images in real-time.

Measurement of edge detection algorithms in clean and noisy environment

Abstract: The subject of identification edge in images has a wide application in various fields for that it's considered one of the important topics in a digital image processing. There are many algorithms to detect the edge in images, but the performance of these algorithms depends on the type of image, the environment of the image and the threshold value of the edge algorithm. The objective of this paper is to evaluate five algorithms of edge detection which are Roberts, Sobel, Prewitt, LOG, and Canny in multi environments clean and noisy by using several types of original images (binary image, graphic image, high frequency image, low frequency image, median frequency image, and texture image) and then determine the best algorithm. In noisy environment the following noises was used Gaussian, salt and pepper and speckle. It's known that each edge detection algorithm has a threshold value, if the current pixel value is less than the defined threshold in strength, it will be considered an edge pixel. The change rate of the threshold value in all environments is also explained through this study.

Study Sobel Edge Detection Effect on theImageEdges Using MATLAB

Abstract: The areas of this work are in digital image process and telecommunication engineering, which are very wide fields. This work is intended to implement the edge detection for digital image, so that it may be carried out to a big contour identification of an image. Edge detection is one of the most fundamental operations in image processing and computer vision. It is defined as the process of locating the boundaries of objects or textures depicted in an image. Knowing the positions of these boundaries is critical in the process of image enhancement, recognition, restoration and compression. The edges of image are considered to be most important image attributes that provide valuable information for human image perception. The data of edge detection is very large, so the speed of image processing is a difficult problem. Sobel operator is commonly used in edge detection. In the edge function, the Sobel method uses the derivative approximation to find edges. This paper mainly used the Sobel operator method to do edge detection processing on the images. Our paper is focused on software used to detect edges of image employing mainly the MATLAB program for solving this problem.

Edge enhancement of depth based rendered images

Abstract: Depth image based rendering is a well-known technology for the generation of virtual views in between a limited set of views acquired by a cameras array Intermediate views are rendered by warping image pixels based on their depth Nonetheless, depth maps are usually imperfect as they need to be estimated through stereo matching algorithms; moreover, for representation and transmission requirements depth values are obviously quantized Such depth representation errors translate into a warping error when generating intermediate views thus impacting on the rendered image quality We observe that depth errors turn to be very critical when they affect the object contours since in such a case they cause significant structural distortion in the warped objects This paper presents an algorithm to improve the visual quality of the synthesized views by enforcing the shape of the edges in presence of erroneous depth estimates We show that it is possible to significantly improve the visual quality of the interpolated view by enforcing prior knowledge on the admissible deformations of edges under projective transformation Both visual and objective results show that the proposed approach is very effective

Bright and dark distance-based image decomposition and enhancement

Abstract: High contrast images are common in the scenes with dark shadows and bright light sources. It is difficult to simultaneously enhance the details in both dark and light areas on most wide dynamic range images. Recently, several image enhancement methods have been proposed to solve this problem. However, most of them are not consistent, may produce unnatural artifacts, and exhibit poor results when images having wide dynamic range are processed. In this paper, a novel method for contrast enhancement of wide dynamic range images is presented. Our method is based on an innovative image decomposition framework. Minimum cross-entropy between bright and dark image components is used to decompose an image into dark and bright image components. Visual and extensive quantitative analysis show that the proposed method outperforms the state-of-the-art algorithms, including the well-known Retinex, Histogram equalization, and Gamma Correction methods. Moreover, the new algorithm can be used in real-time image processing systems due to its simplicity and low computational complexity. The proposed method has various applications such as video door phone, security video cameras, and others. It is possible to be utilized in electronic products and image related instrumentation.

Interferometric microscope with true color imaging

Abstract: Optical 3D profilers based on Coherence Scanning Interferometry (CSI) provide high-resolution non-contact metrology for a broad range of applications. Capture of true color information together with 3D topography enables the detection of defects, blemishes or discolorations that are not as easily identified in topography data alone. Uses for true color 3D imaging include image segmentation, detection of dissimilar materials and edge enhancement. This paper discusses the pros and cons of color capture using standard color detectors and presents an alternative solution that does not rely on color filters at the camera, thus preserving the high lateral and vertical resolution of CSI instruments.

Improved canny edge detector in various color spaces

Abstract: Edge detection is one of the most commonly used operations in the field of image processing. The traditional Canny edge detector is widely used in gray-scale image processing. However, this traditional edge detector is unable to deal with color images efficiently. The problem arises due to the double threshold technique: hysteresis threshold. In this paper, we have developed a new double threshold technique. Using this threshold, results of Canny edge detector in different color spaces like RGB, HSV and L*a*b* have been analyzed for accuracy and computational speed. Experiments show that the improved Canny edge detector gives better performance than the traditional Canny edge detector in all color spaces. Also the L* component of L*a*b* color space gives the best edge details with least computational cost in comparison to edge detection in all other color spaces.

System and method for single-frame based super resolution interpolation for digital cameras

Abstract: A digital camera system for super resolution image processing is provided. The digital camera system includes a resolution enhancement module configured to receive at least a portion of an image, to increase the resolution of the received image, and to output a resolution enhanced image and an edge extraction module configured to receive the resolution enhanced image, to extract at least one edge of the resolution enhanced image, and to output the extracted at least one edge of the resolution enhanced image, the at least one edge being a set of contiguous pixels where an abrupt change in pixel values occur. The digital camera system also includes an edge enhancement module configured to receive the resolution enhanced image and the extracted at least one edge, and to combine the extracted at least one edge or a derivation of the extracted at least one edge with the resolution enhanced image.

Fractional vortex dipole phase filter

Abstract: In spatial filtering experiments, the use of vortex phase filters plays an important role in realizing isotropic edge enhancement. In this paper, we report the use of a vortex dipole phase filter in spatial filtering. A dipole made of fractional vortices is used, and its filtering characteristics are studied. It is observed that the filter performance can be tuned by varying the distance of separation between the vortices of the dipole to achieve better contrast and output noise suppression, and when this distance tends to infinity, the filter performs like a 1-D Hilbert mask. Experimental and simulation results are presented.

Conditional Toggle Mappings: Principles and Applications

Abstract: We study a class of mathematical morphology filters to operate conditionally according to a set of pixels marked by a binary mask. The main contribution of this paper is to provide a general framework for several applications including edge enhancement and image denoising, when it is affected by salt-and-pepper noise. We achieve this goal by revisiting shock filters based on erosions and dilations and extending their definition to take into account the prior definition of a mask of pixels that should not be altered. New definitions for conditional erosions and dilations leading to the concept of conditional toggle mapping. We also investigate algebraic properties as well as the convergence of the associate shock filter. Experiments show how the selection of appropriate methods to generate the masks lead to either edge enhancement or salt-and-pepper denoising. A quantitative evaluation of the results demonstrates the effectiveness of the proposed methods. Additionally, we analyse the application of conditional toggle mapping in remote sensing as pre-filtering for hierarchical segmentation.

Edge enhancement in multispectral satellite images by means of vector operators

Abstract: Resumen Edge enhancement is an element of analysis to derive the spatial structure of satellite images. Two methods to extract edges from multispectral satellite images are presented. A multispectral image is modeled as a vector field with a number of dimensions equal to the number of bands in the image. In this model, a pixel is defined as a vector formed by a number of elements equal to the number of bands. Two vector operators are applied to such vector field. In our first method, we extend the definition of the gradient. In this extension, the vector difference of the window central pixel with neighboring pixels is obtained. A multispectral image is then generated where each pixel represents the maximum change in spectral response in the image in any direction. This image is named a multispectral gradient. The other method, considers the generalization of the Laplacian by means of an h-dimensional Fourier transform. This image is named a multispectral Laplacian. The vector operators perform a simultaneous extraction of edgecontent in the spectral bands of a multispectral image. Our methods are parameter-free. Our methods work for a multispectral image of any number of bands. Two examples are discussed that involve multispectral satellite images at two scales. We compare our results with widely used edge enhancement procedures. The evaluation of results shows better performance of proposed methods when compared to widely used edge operators. Palabras clave: edge detection, multispectral image, edge enhancement, vector operator.

Brightness preserving Bi-histogram equalization using edge pixels information

Abstract: Image enhancement plays an important role to the image or video display systems. It is used to process an input image and the output image is more pleasing than the input one. Many contrast enhancement techniques proposed so far. Among them histogram equalization (HE) is widely used for contrast enhancement technique. In this paper, we propose a new technique of histogram equalization method which preserves the brightness of the image. We tested the proposed method on a variety of images. The proposed method is also compared with some other related methods. We also use some parameters for comparison and the proposed method gives better performance than the related methods.

Applications of edge preservation ratio in image processing

Abstract: Edge preservation ratio (EPR) is a full-reference metric for objective image quality assessment (IQA). It is under the assumption that key messages to human visual systems are mainly from image structures, and these structures can be extracted by edge detection. EPR measure is twofold: accuracy and robustness, and a color map is synthesized to reveal structure changes before and after image processing. The feasibility and superiority of EPR have been validated via image magnification and noise reduction. Experimental results suggest that: (1) it is challenging to fully recover lost messages by image magnification; (2) high image contrast may be derived from concise and distinct image structures.