Edge enhancement

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

Method and device for removing CT (computed tomography) image noises

Abstract: The invention discloses a method and a device for removing CT (computed tomography) image noises, and relates to the technical field of image noise removal. The method comprises the following steps of: estimating the tissue weight of an image, estimating the noise level of the image, calculating a noise removing parameter, carrying out anisotropic diffusion filtering on the image, carrying out edge enhancement on the image subjected to filtering output, enhancing details of the image and correcting the contrast ratio, cutting the image, and outputting the result. The device comprises a module for estimating the tissue weight of the image, a module for estimating the noise level of the image, a module for calculating the noise removing parameter, a module for carrying out anisotropic diffusion filtering on the image, a module for carrying out edge enhancement on the image subjected to filtering output, a module for performing detail enhancement and contrast ratio rectification on the image, and a module for cutting images and outputting the result. The method and the device can maintain the image edge and the original contrast ratio of the image while effectively removing the high-frequency noises of the CT images.

Optoelectronic associative memory

Abstract: An associative optical memory including an input SLM in the form of an edge enhanced LCLV and a pair of memory SLMs in the form of LCTVs forms a matrix array of an input image which is cross correlated with a matrix array of stored images. The correlation product is detected and non-linearly amplified to illuminate a replica of the stored image array to select the stored image correlating with the input image. The LCLV is edge enhanced by reducing the bias frequency and voltage and rotating its orientation. The edge enhancement and non-linearity of the photodetection improves the orthogonality of the stored images. The illumination of the replicate stored image provides a clean stored image, uncontaminated by the image comparison process.

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.

A method for reconstructing patterns of somatosensory cerebral cortical activity.

Abstract: An interactive graphics package was developed in order to acquire, display, and manipulate images of cerebral cortical autoradiographic data. The primary purpose for development of the system was to reconstruct accurate 2-dimensional maps of the functional activity within the somatosensory cerebral cortex. A Datacube Q-bus graphics module (QVG/QAF-123) was interfaced with the Micro PDP-11/23 to accept a standard RS170 video input signal, and autoradiographs of serial sections (each 20 microns thick) of a cerebral cortex were digitized individually to 768 X 512 X 8 bit resolution. Input look-up tables were used to standardize the autoradiographic data. Boundaries of the somatosensory cortex were entered (with a Summagraphics MM 1201 digitizer), and the image data was stored on disk file (a method of data compression was devised). A method for segmenting the image data for many (sequential) sections was developed that provided arrays from which the maps were generated. Thresholding, histogram equalization, edge detection and edge enhancement, and filters in both the spatial and frequency domains were employed to process the images of the maps. Plots of optical density values along any axis of the maps and gray level histograms of any map region could also be generated. Maps made by the described method are much higher in resolution than those produced by traditional (manual) methods, and permit analysis of the reconstructions in both the frequency and spatial domains.