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
More filters
TL;DR: Based on the Jones calculus of polarization states and Fourier analysis, the authors calculate and analyze the point spread function of an optical 4-f system including an S-waveplate filter having the vectorial vortex of topological charge 1 (TC = 1).
Abstract: We propose to use a super-structured waveplate (called an S-waveplate) for vectorial optical vortex filtering, and experimentally demonstrate the radial Hilbert transform and selective edge enhancement. Based on the Jones calculus of polarization states and Fourier analysis, we calculate and analyze the point spread function of an optical 4-f system including an S-waveplate filter having the vectorial vortex of topological charge 1 (TC = 1). Numerical simulations and optical experiments demonstrate that a vectorial optical vortex filter can be used to implement selective edge enhancement with an analyzer before the output plane. The edge enhancement can be obtained even when the center of the filter is off-axis or the illuminating light is non-monochromatic.
23 citations
Patent•
15 Feb 2008
TL;DR: In this article, the edge component computed at edge enhancement block ( 106 ) is sent out to YC synthesis block ( 110 ) and color saturation correction block ( 109 ) respectively.
Abstract: The digital camera ( 100 ) has an image processor that corrects an input image for a spatial frequency band. The edge enhancement block ( 106 ) computes an edge component for band correction. A signal interpolated at color interpolation block ( 107 ) to make compensation for a color component missing from each pixel of a single-chip image is then converted at YC transform block ( 108 ) into a luminance signal and a color difference signal after tone correction, the luminance and color signals sent out to YC synthesis block ( 110 ) and color saturation correction block ( 109 ), respectively. Then, the color saturation correction block ( 109 ) controls the color saturation of the color difference signal to send it out to YC synthesis block ( 110 ). At the same time, the edge component computed at edge enhancement block ( 106 ) is sent out to YC synthesis block ( 110 ), too.
23 citations
01 Jul 2008
TL;DR: This paper proposes a cost-effective solution for combined de-noising and sharpening of digital images that combines the unsharp masking and sigma filtering techniques through a regularization mechanism thus ensuring effective noise reduction and edge enhancement in the processed image.
Abstract: In this paper we present a cost-effective solution for combined de-noising and sharpening of digital images. Our method combines the unsharp masking and sigma filtering techniques through a regularization mechanism thus ensuring effective noise reduction and edge enhancement in the processed image. We describe our method in detail and we analyze the proposed implementation through extensive experiments done in various scenarios. Due to its low computational complexity the proposed method is well suited for mobile implementations.
23 citations
Journal Article•
TL;DR: In this paper, the three best methods for enhancing geological structure were found to be: (1) a simple linear contrast stretch; (2) a mean or median low-pass filter to reduce speckle prior to edge enhancement; (3) a K nearest-neighbor average to cosmetically reduce specks; and (4) a modification of the Moore-Waltz (1983) technique.
Abstract: Various digital enhancement techniques for SAR are compared using SIR-B and Seasat images of the Canadian Shield. The three best methods for enhancing geological structure were found to be: (1) a simple linear contrast stretch; (2) a mean or median low-pass filter to reduce speckle prior to edge enhancement or a K nearest-neighbor average to cosmetically reduce speckle; and (3) a modification of the Moore-Waltz (1983) technique. Three look directions were coregistered and several means of data display were investigated as means of compensating for radar azimuth biasing.
23 citations
01 Dec 2011
TL;DR: The experimental results on various Soil textures clearly demonstrate the efficiency of the proposed methods, and the features are constructed on preprocessed methods applied on the Soil texture image by considering different types of windows.
Abstract: Texture analysis has been used for recognizing synthetic and natural textures. Textures are one of the important features in computer vision for image classification and retrieval. An important approach to region description is to quantify its texture content. In this paper ,the Soil images has been analyzed using various image pre processing tasks such as Gray level thresholding, Low pass filter, Edge enhancement using Prewitt's Horizontal filtering and then Feature extraction using 3x3 Law's mask convolution. The features are constructed on preprocessed methods applied on the Soil texture image by considering different types of windows. These features offer a better classification rate. The experimental results on various Soil textures clearly demonstrate the efficiency of the proposed methods.
23 citations