Edge enhancement

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

Fuzzy anisotropic diffusion based on edge detection

Abstract: A fuzzy anisotropic diffusion algorithm based on edge detection and noise estimation is proposed for image denoising and edge enhancement. The edginess and noisiness fuzzy membership values are calculated with the edge detector and noise deviation of center pixel from the neighboring average, respectively. The employed edge detector provides more accurate estimation of edges and is less sensitive to noise than the gradient operator in anisotropic diffusion. Taking noise into account ensures that the diffusion process works well regardless of the type of noise degradation, and effectively reduces the number of iterations. We demonstrate how the rather complicated edge detection and noise estimation can be put together through fuzzy inference and embedded into anisotropic diffusion to provide better control on the diffusion processing. Quantitative and qualitative evaluations demonstrate superior performance of the proposed fuzzy approach while processing images with additive and multiplicative noise.

Thin edge detector

Abstract: Most edge detectors are based on detecting points in the image with a high gradient value. However, in general, they omit the idea that the edge must be a connected line of pixels. Instead of that, these techniques normally use a global threshold value, to control the quantity of edge points detected. Due to this, many other edge points are detected, but some of them do not really belong to an edge. In this paper we propose a new edge detector, that detects entire pieces of 1-pixel-thick lines. This algorithm is more robust concerning the choice of the threshold value, because it works with the average gradient value of each piece of line. Two previous filters are needed: a contrast process, to enhance the edges, and a morphological denoising to remove noise but without blurring the edges in the image. The proposed algorithm starts from pixels with a high gradient value, and follows neighbor pixels connecting new points.

Image processing method and apparatus empolying the same

Abstract: An image processing apparatus obtains image data with a low resolution by processing a plurality of pixel values read in with a high resolution into one pixel value, for the low resolution mode. Thereby, the image processing apparatus is able to transmit unfailingly image data needed for reproduction of a narrow line in a document even during a low-resolution mode. Further, in accordance with a resolution mode that is currently selected, an image processing apparatus adjusts the ratio between the amount of edge enhancement with respect to the main scanning direction and the amount of edge enhancement with respect to the sub-scanning direction, thus unfailingly including the image data needed for reproduction of a narrow line extending in the main and/or sub-scanning direction.

Image Contrast Enhancement Algorithm Based on GM(1,1) and Power Exponential Dynamic Decision

Abstract: Image enhancement processing is a very important operation during image preprocessing. Compared with to enhancc the overall contrast level of image, enhancing the local contrast of image can improve the level of such contrast directly as well as the quality and effect of image enhancement. In this paper, the gray prediction model is applied to the process of enhancing image local contrast, so as to measure the change range of image local contrast and adaptively adjust the scale of enhancing image local contrast. The simulation results show that, in addition to enhancing the contrast of gray level on the edge of image, the proposed algorithm can inhibit roughened nonedge region and improve the quality of local enhancement processing, which create a more favorable condition for the further image edge detection.