Edge enhancement

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

Image Denoise Based on Soft-Threshold and Edge Enhancement

Abstract: In order to eliminate noise and preserve the edge and texture detail in image, this article proposes a method of image denoise based on soft-threshold and edge enhancement. To enhance edge, first we use the canny edge detection operator to detect the feature of the image, then we preprocess the edge image with a flat operator, next we use stationary wavelet transform to process the processed edge image and noisy image, then we add these wavelet coefficients at the corresponding level, so we can use the soft threshold denoise technology to eliminate the noise. Since the feature is enhanced before soft threshold denoise, the feature can be preserved well. From the result we can see that comparing to the wiener filter, this denoise method has visually pleasing image and the PSNR is improved. Comparing the traditional soft threshold denoise technology, this method has 1-2dB improvement in PSNR.

Edge enhancement using modified edge boost function

Abstract: A method of edge enhancing a digital image having pixels, includes acquiring a digital image; computing an edge boost function having positive and negative boost for different portions of the edge of the digital image; adjusting the edge boost function to produce a modified edge boost function such that the gain of the negative boost is greater than the gain of the positive boost; and applying the modified edge boost function to the digital image to provide an edge enhanced digital image.

Adaptive edge enhancing technique of impulsive noise removal in color digital images

Abstract: In this paper a novel class of noise attenuating and edge enhancing filters for color image processing is introduced and analyzed. The proposed adaptive filter design is minimizing the cumulative dissimilarity measure of a cluster of pixels belonging to a sliding filtering window and outputs the most centrally located pixel. The proposed filter is computationally efficient, easy to implement and very effective in suppressing impulsive noise, while preserving image details and enhancing its edges. Therefore it can be used in any application in which simultaneous denoising and edge enhancement is a prerequisite for further steps of the color image processing pipeline.

Frequency Domain Image Analysis for Detecting Stress Cracks in Corn Kernels

Abstract: A corn kernel classification procedure was developed in the frequency domain using a two-dimensional Fourier Transform for inspection of stress cracks. Investigations were also conducted to define suitable conditions and optimum image resolution for viewing stress cracks in corn kernels using a computer vision system. A pre-processing procedure included contrast enhancement, edge enhancement, and kernel edge elimination to improve stress crack recognition. A Fast Fourier Transform algorithm was applied to the pre-processed images, and the transformation results were condensed into 33 feature signatures representing position or orientation invariant morphological features. A multi-variate discriminant analysis and multiple regression analysis were used to develop classification criteria for stress crack inspection. Both methods were able to detect stress cracks satisfactorily with an average success ratio above 96%.

Blobs and Cracks Detection on Plain Ceramic Tile Surface

Abstract: Defects detection on ceramic tiles is a major issue in the ceramic tile industry to maintain the Quality of tiles. On the other hand maintaining the production rate with respect to time is also a major in the ceramic tile industry. Considering these criteria an efficient surface defects detection technique has proposed in this paper that can ensure better quality of tiles in the production process as well as production rate is also improved. Our proposed method plays an important role to maintain the quality standards in the ceramic tile industry. This proposed method detects the surface defects such as blobs and cracks on ceramic tiles in a very short period of time with high accuracy. In ceramic tile production process many stages lead to different types of faults and defects on the final product. These defects may occur due to chemical impurities in the material or due to some physical faults in the production process. At present the most of the phases of ceramic tile production are automated, but still the last phase of tile inspection is done manually. For inspection of ceramic tiles, the industry requires the human experts. Experts may have different opinions about the presence of defects. The capability of the human depends on training, knowledge and experience. The drawback to the manual inspection is that, human can work for a limited time and easily get tired within a few hours. The judgment of human is affected by fatigue. On the other hand the production process runs continuously in the factories, but inspection system doesn"t match with the rate of production. So, the need of automated system comes into the picture. The automated system is free from tiredness and the inspection is done with the same efficiency for all tiles. The blobs and cracks are common defects that we easily found on the ceramic tile surface. Considering all the problems related to tile inspection, an efficient method is developed for ceramic tile inspection, which can detect blobs and cracks very efficiently on the surface of ceramic tiles. The proposed method is based on image processing and morphological operations. The objective of the research paper is to propose an efficient defect detection technique which can find out surface defects on images of tiles with high accuracy and within a very short time. The proposed paper is organized as, section 2 represents literature review. In section 3 gives a brief overview of techniques used in proposed method. Section 4 provides the explanation of the proposed approach. In section 5 and section 6 experimental results and conclusion is mentioned. II. Related Work Throughout the last decade many kinds of defect detection systems have been developed. These systems have developed to identify the various kinds of defects on ceramic tiles and applied to industrial process. D.O. Aborisade (3) has suggested a method for automatic surface inspection of plain ceramic wall tile, which is based on computer vision. The algorithm starts with an application of edge enhancement operation to input image in order to highlight the edges of the objects in the images of tiles. Image edge enhancement and detection algorithms are applied using a spatial convolution process. A 3X3 mask is used to measure the gradient and the direction of the gradient in the image. The gradient at a specific location is threshold to obtain the edge map of the defect. After segmentation of cracks from the image some discriminant functions were developed. To identify the tiles into either a defect class or a reject class based on the feature extracted at the real time. In H. Elbehiery, A. Hefnawy, and M. Elbehiery have proposed an algorithm for surface defect detection on ceramic tiles which is based on Image processing and morphological techniques (4). First part of the algorithm starts with image acquisition, and then histogram equalization is performed on the images. The intensity adjustable histogram equalized images are the input to the second part of the algorithm. The second part of the main algorithm includes many of the individual complementary algorithms differ due to the various kinds of defects. For cracks and long crack detection the input images are converted to black/white images. An edge detection operation is performed to detect the defect pixels. Some morphological operations have been done to discriminate the defect pixels more accurately followed by noise reduction and smoothing object processing to get clear image containing the only defect. Pinholes are detected using grayscale morphological operations followed by noise reduction processing to get a clear image for the defects. Similar approach is used for blob detection. Their algorithm requires much computational time because for each type of defect the