Topic
Edge enhancement
About: Edge enhancement is a research topic. Over the lifetime, 2324 publications have been published within this topic receiving 30962 citations.
Papers published on a yearly basis
Papers
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TL;DR: A method of spatial filtering in the frequency domain which enhances edges and boundaries, thus making small urban features, such as parks, tree-lined streets and new housing developments, visible on digital images with, for example, 30 m resolution is described.
Abstract: We describe a method of spatial filtering in the frequency domain which enhances edges and boundaries, thus making small urban features such as parks, tree-lined streets and new housing developments, visible on digital images with, for example, 30 m resolution. Hitherto, while satellite imagery has been useful because of its large area and repetitive coverage, the spatial resolution for multiband imagery has been such that it precluded the detailed studies which may now be possible. While spatial frequency filtering, edge enhancement, high-boost and directional filtering have been possible, this has generally involved the use of a convolution matrix whose elements have been defined from general empirical rules. Frequency domain operations offer the advantage of selectively tailoring the filter in order to enhance certain features.
8 citations
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21 Apr 1998TL;DR: The paper reports an edge detection operator which is fast in application to grey scale images and is sensitive to 'difficult' edges but not sensitive to artifacts such as scanner interlacing artifacts.
Abstract: Edge detection is a fundamental step in many machine vision or image processing applications and systems. The importance of edge detection increases as one seeks to increase the level of automation in the image processing system. The paper reports an edge detection operator which is fast in application to grey scale images and is sensitive to 'difficult' edges but not sensitive to artifacts such as scanner interlacing artifacts. The edge detection operator presented is simple but provides good detection of edges and clean and continuous edges across most of the image.
8 citations
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TL;DR: In order to handle the problem that the contrast decreases in the dark region caused by underexposure, this detector uses a self-adjusting threshold, so that it can detect the edge in regions of different grey background correctly.
Abstract: This paper presents a new edge detector using 5×5 mask, which can reduce noise efficiently but not increase the width of the detected edge which always happens in the case of using the 5×5 window edge detector. Besides, in order to handle the problem that the contrast decreases in the dark region caused by underexposure, this detector uses a self-adjusting threshold, so that it can detect the edge in regions of different grey background correctly
8 citations
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TL;DR: This paper proposes a novel image enhancement method which makes use of these smoothed images produced by diffusion methods and shows the higher performance, both on the basis of subjective evaluation and objective measures, of the proposed method over current methods.
Abstract: Enhancing an image in such a way that maintains image edges is a difficult problem. Many current methods for image enhancement either smooth edges on a small scale while improving contrast on a global scale or enhance edges on a large scale while amplifying noise on a small scale. One method which has been proposed for overcoming this is anisotropic diffusion, which views each image pixel as an energy sync which interacts with the surrounding pixels based upon the differences in pixel intensities and conductance values calculated from local edge estimates. In this paper, we propose a novel image enhancement method which makes use of these smoothed images produced by diffusion methods. The basic steps of this algorithm are: a) decompose an image into a smoothed image and a difference image, for example by using anisotropic diffusion or as in Lee's Algorithm [14]; b) apply two image enhancement algorithms, such as alpha rooting [7] or logarithmic transform shifting [15]; c) fuse these images together, for example by weighting the two enhanced images and summing them for the final image. Computer simulations comparing the results of the proposed method and current state-of-the-art enhancement methods will be presented. These simulations show the higher performance, both on the basis of subjective evaluation and objective measures, of the proposed method over current methods.
8 citations
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TL;DR: A new method for ring artifact removal and edge enhancement for industrial CT images based on the multi-resolution techniques such as, discrete wavelet transform (DWT), stationary wavelettransform (SWT) and dual tree complex wavelet Transform (DT-CWT) is proposed.
8 citations