Edge enhancement

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

A New Text Extraction Method Incorporating Local Information

Abstract: Text detection and extraction in images with complex background can provide useful information for video annotation and indexing. More attention is paid to text detection for its importance, but text extraction is necessary for the text recognition, and it can test the validity of text detection. In this paper, we conclude text extraction is to segment the image and to remove noises, and then a robust text extraction method incorporating local information is proposed. First, we get the gray image from the original image and reprocess the gray image with edge enhancement. Then a binarization method incorporating local information is used to segment the gray image, by which the text-noises are removed and a binary image is obtained. Finally, the connected component analysis based on the character's density and geometric feature is performed on the binary image, by which background-noises are removed. The preliminary experiments show some promising results.

Side-scan sonar mapping and computer-aided interpretation in the Santa Barbara Channel, California

Abstract: We have experimented with digital processing of side scan sonar data taken in a 14 sq-km area of continental shelf offshore Southern California. The data were FM tape recorded during the survey and digitized and processed later in the laboratory. The digital image processing included both image correction and image enhancement. Geometric corrections were applied to correct for image distortions due to variable ship position and speed and sonar slant range. Enhancements that were tried included contrast stretching, band-pass filtering, image restoration (inverse filtering), and various edge enhancements such as density slicing and standard deviation filters. Interpretive procedures were also attempted and included digital mosaicking, stereoscopic viewing, and falsecolor display. The most effective processing was geometric correction combined with contrast stretching. Mosaicking proved difficult due to imprecise navigation (±50 m), but was very effective in increasing the understanding of the geologic structure in the survey area.

Method and device for processing image noise

Abstract: The invention provides a method and a device for processing image noise. The method comprises the steps of: obtaining a Y-component image of a YUV space image corresponding to a frame of original image; decomposing the Y-component image by adopting a preset image decomposition algorithm to obtain a corresponding high frequency domain signal and a corresponding low frequency domain signal; identifying an edge signal and a non-edge signal in the high frequency domain signal; carrying out edge enhancement processing for the edge signal and filtering the non-edge signal by using a first filtering method to obtain the processed high frequency domain signal; filtering the low frequency domain signal by using a second filtering method to obtain the processed low frequency domain signal; reconstructing the processed high frequency domain signal and the processed low frequency domain signal by using an image reconstruction algorithm which corresponds to the preset image decomposition algorithm so as to obtain a de-noised Y-component image. The method for removing the image noise, provided by the invention, can efficiently remove a plenty of noise in the image, and can improve Signal to Noise Ratio (SNR) of the image.

Analysis and application of image segmentation and edge detection operator

Abstract: Edges are the main feature of image, also an essential part of the computer visual and pattern recognition, so edge detection is a crucial step in the process of image processing. This article analyzes the algorithm of image segmentation and edge detection, compared the advantages and disadvantages of various operators by MATLAB. Through the experimental comparison, The overall effect of Canny operator is relatively well, but less detailed. So there is not an universal edge operator. The most important is how to choose a suitable threshold, this will be a decisive role.

Enhancement of Solar Corona and Comet Details

Abstract: Finely detailed striae in astronomical images can be important in formulation of theory. Examples are studies of streamers in the solar corona and of dust tails in comets. In both instances, conventional observations fail to reveal much of the structural detail. Digital image processing has been used at Los Alamos Scientific Laboratory (LASL) for enhancing these images. The corona images have tremendous variations in film density which must be eliminated before fine striae can be seen. These variations can be removed by means of numerical modeling of their spatial relation to the sun. This model can be thought of as a surface of background film density. In the comet images the overall variation is less severe. Further, the large number of comet images makes it infeasible to model them individually. Hence, an extreme low-pass filter was used to create an image which can be used as the background surface. In both cases, the background surface is divided into the original image pixel by pixel. This quotient image is then frequency-filtered for edge enhancement or noise control. Nonlinear density transformations are then used to enhance contrast. For both types of images, heretofore unmeasurable details become readily visible for analysis.