Topic
Edge enhancement
About: Edge enhancement is a research topic. Over the lifetime, 2324 publications have been published within this topic receiving 30962 citations.
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22 Feb 2000
TL;DR: The proposed method overcomes the drawbacks of the conventional gradient methods for edge detection such as Prewitt and Sobel methods, and automatically obtains four threshold values, and apply fuzzy reasoning for edge enhancement.
Abstract: A modified fuzzy Sobel method for edge detection and enhancement is proposed. This method is a modification of the fuzzy Sobel method proposed by Kuo, Lee and Liu see (IEEE Conference on Fuzzy Systems, p.1069-74, 1997). The proposed method overcomes the drawbacks of the conventional gradient methods for edge detection such as Prewitt and Sobel methods. It automatically obtains four threshold values, and apply fuzzy reasoning for edge enhancement. The edges extracted by this method are very clear and provides better representation for image edges and object contours.
59 citations
Patent•
09 Apr 1991
TL;DR: In this article, a reverse normalizing section reversely normalizes picture data outputted by the decoding section, while an edge enhancing section executes edge enhancement by using the edge filter and a data counting section counts coded data existing in a plurality of blocks constituting the digital picture data block by block.
Abstract: A device for decoding digital picture data coded by compression to thereby regenerate original picture data has a decoding section for decoding the digital picture data. A reverse normalizing section reversely normalizes picture data outputted by the decoding section. A reverse orthogonal transforming section executes two-dimensional reverse orthogonal transform with picture data outputted by the reverse normalizing section. A data counting section counts coded data existing in a plurality of blocks constituting the digital picture data block by block. A filter selecting section selects an interblock filter and an edge filter on the basis of the amount of coded data determined by the data counting section block by block. A correcting section corrects a distortion between nearby blocks by using the interblock filter selected, while an edge enhancing section executes edge enhancement by using the edge filter. The filter selecting section selects the intensity of the interblock filter and the intensity of the edge filter on the basis of the amount of coded data determined block by block. The filters for the correction of a distortion may be replaced with predicted values which are determined on the basis of the activities of individual blocks and substituted for pixel data. Random noise matching the activities of individual blocks may be added to pixel data after the correction in order to eliminate the distortion of contours of the blocks.
58 citations
TL;DR: The potential of image processing techniques will be illustrated by reference to the post-Palaeolithic rock art of the site of Selva Pascuala (Cuenca, Spain), which has great potential for the study of rock art.
58 citations
TL;DR: In this article, the authors converted spare-domain images into timedomain images using diffraction from dynamic holograms inside a Fourier pulse shaper, with photorefractive quantum wells (QW's) used as the dynamic holographic medium.
Abstract: Coded ultrafast optical pulses can be treated as one-dimensional (1-D) images in the time domain. We have converted spare-domain images into time-domain images using diffraction from dynamic holograms inside a Fourier pulse shaper, with photorefractive quantum wells (QW's) used as the dynamic holographic medium. We present several examples, in which amplitude or phase modulation of the hologram writing beams modifies the complex spectrum of the femtosecond output, resulting in a time-domain image. Both storage and processing of time-domain images can be achieved, depending on the hologram writing geometry and power densities. Time-domain processing operations such as edge enhancement, Fourier transform, and correlation are demonstrated.
57 citations
TL;DR: Experimental results confirm that the proposed guided image contrast enhancement framework can efficiently create visually-pleasing enhanced images which are better than those produced by the classical techniques in both subjective and objective comparisons.
Abstract: We propose a guided image contrast enhancement framework based on cloud images, in which the context- sensitive and context-free contrast is jointly improved via solving a multi-criteria optimization problem. In particular, the context-sensitive contrast is improved by performing advanced unsharp masking on the input and edge-preserving filtered images, while the context-free contrast enhancement is achieved by the sigmoid transfer mapping. To automatically determine the contrast enhancement level, the parameters in the optimization process are estimated by taking advantages of the retrieved images with similar content. For the purpose of automatically avoiding the involvement of low-quality retrieved images as the guidance, a recently developed no-reference image quality metric is adopted to rank the retrieved images from the cloud. The image complexity from the free-energy-based brain theory and the surface quality statistics in salient regions are collaboratively optimized to infer the parameters. Experimental results confirm that the proposed technique can efficiently create visually-pleasing enhanced images which are better than those produced by the classical techniques in both subjective and objective comparisons.
57 citations