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Edge enhancement

About: Edge enhancement is a research topic. Over the lifetime, 2324 publications have been published within this topic receiving 30962 citations.


Papers
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Proceedings ArticleDOI
23 Mar 1995
TL;DR: A new filter is introduced that will render corners, as well as edges, invariant to the diffusion process, because many edges in images are not isolated the corner model better represents the image than the edge model.
Abstract: We have recently proposed the use of geometry in image processing by representing an image as a surface in 3-space. The linear variations in intensity (edges) were shown to have a nondivergent surface normal. Exploiting this feature we introduced a nonlinear adaptive filter that only averages the divergence in the direction of the surface normal. This led to an inhomogeneous diffusion (ID) that averages the mean curvature of the surface, rendering edges invariant while removing noise. This mean curvature diffusion (MCD) when applied to an isolated edge imbedded in additive Gaussian noise results in complete noise removal and edge enhancement with the edge location left intact. In this paper we introduce a new filter that will render corners (two intersecting edges), as well as edges, invariant to the diffusion process. Because many edges in images are not isolated the corner model better represents the image than the edge model. For this reason, this new filtering technique, while encompassing MCD, also outperforms it when applied to images. Many applications will benefit from this geometrical interpretation of image processing, and those discussed in this paper include image noise removal, edge and/or corner detection and enhancement, and perceptually transparent coding.

20 citations

Patent
26 Apr 1996
TL;DR: In this article, a video signal processing circuit for processing video signal including a noise is proposed, which comprises an edge enhancement signal generation circuit, a noise reduction circuit for reducing a level of the noise to generate a noise reduced video signal, and an adder for adding the edge enhancing signal to the noise reduction video signal to output a processed video signal.
Abstract: A video signal processing circuit for processing a video signal including a noise is disclosed which comprises: an edge enhancement signal generation circuit for generating an edge enhancement signal from the video signal; a noise reduction circuit for reducing a level of the noise to generate a noise reduced video signal; and an adder for adding the edge enhancement signal to the noise reduction video signal to output a processed video signal. The apparatus may further comprise an input terminal for receiving the video signal, wherein the edge enhancement signal generation circuit and the noise reduction circuit coupled to the input terminal are in parallel. This apparatus may further comprise a first delay circuit for delaying the video signal by one horizontal line period to generate a 1-H delayed video signal and a second delay circuit responsive to the video signal for generating a 2-H delayed video signal, wherein the edge enhancement signal generation circuit generates the edge enhanced signal from the video signal, the 1-H delayed video signal, and the 2-H delayed video signal and the noise reduction circuit generates the noise reduced video signal from the video signal, the 1-H delayed video signal, and the 2-H-delayed video signal.

20 citations

Journal ArticleDOI
TL;DR: A Particle Swarm Optimization (PSO)-based feature enhancement approach in the wavelet domain for noisy image segmentation that helps to enhance intensity features for clustering-based denoising, and also provides adaptivity for the system that performs well on a range of real, synthetic, and simulated noisy images with different noise levels and range/spatial properties.
Abstract: Noisy image segmentation is a hot topic in natural, medical, and remote sensing image processing. It is among the non-trivial problems of computer vision having to address denoising and segmentation at the same time. Fuzzy C-means (FCM) is a clustering algorithm that has been shown to be effective at dealing with both segmentation-oriented denoising and segmentation at the same time. Moreover, with a high level of noise and other imaging artifacts, FCM loses its ability to perform image segmentation effectively. This paper introduces a Particle Swarm Optimization (PSO)-based feature enhancement approach in the wavelet domain for noisy image segmentation. This approach applies adaptive wavelet shrinkage using FCM clustering performance as an evaluation mechanism and also as the segmentation algorithm. The PSO-based process helps to enhance intensity features for clustering-based denoising, and also provides adaptivity for the system that performs well on a range of real, synthetic, and simulated noisy images with different noise levels and range/spatial properties. Furthermore, the algorithm applies edge enhancement based on Canny edge detector in order to further improve accuracy. Experiments are presented using three different datasets each degraded with different types of common noise. The presented algorithms show effective and consistent performance over a range of severe noise levels without the need for any parameter tuning.

20 citations

Journal ArticleDOI
TL;DR: A novel algorithm, histogram shifting (HS) is presented, which performs edge detection or edge enhancement with the choice of two parameters without introducing "double-edge" effects often obtained with conventional edge-detection algorithms.
Abstract: A novel algorithm, histogram shifting (HS) is presented, which performs edge detection or edge enhancement with the choice of two parameters. The histogram of a region surrounding each pixel is found and translated toward the origin, resulting in the new pixel value. Images from a variety of medical imaging modalities were processed with HS to perform detection and enhancement of edges. Comparison with results obtained from conventional edge detection (e.g., Sobel) and with conventional edge‐enhancement algorithms is discussed. HS appears to perform the edge‐detection operation without introducing ‘‘double‐edge’’ effects often obtained with conventional edge‐detection algorithms. HS also appears to perform edge enhancement without introducing extensive noise artifacts, which may be noticeable with many edge‐enhancement algorithms.

20 citations

Journal ArticleDOI
TL;DR: A three level Gaussian and Laplacian pyramids are constructed to represent the image in different resolution and the performance measure, peak signal to noise ratio proves that the unsharp masking method applied to difference images of LaPLacian pyramid outperforms the other image enhancement methods.
Abstract: Acoustic images captured by side scan sonar are normally affected by speckle noise for which the enhancement is required in different domain. The underwater acoustic images obtained using sound as a source, basically contain seafloor, sediments, living and non-living resources. The Multiresolution based image enhancement techniques nowadays play a vital role in improving the quality of the low resolution image with repeated patterns. Image pyramid is the representation of an image at various scales. In this work, a three level Gaussian and Laplacian pyramids are constructed to represent the image in different resolution. The multiscale representation requires different filters at different scales. The contrast of each image in Gaussian and Laplacian pyramids are improved by applying both histogram equalization and unsharp masking method. The sharpened images are used to reconstruct the enhanced image. The performance measure, peak signal to noise ratio proves that the unsharp masking method applied to difference images of Laplacian pyramid outperforms the other image enhancement methods.

20 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231
20228
202148
202061
201947
201851