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The edge area is especially essential for cutting tools, since this is the contact zone between the work piece and the tool. Hard coatings (PVD or CVD coatings) can protect the edge against wear and they are commonly used. The geometries of the cutting edges change during the coating process, with the edge radius increasing. Therefore, the film thickness is limited and the initial radius of the uncoated tool must be smaller than the target radius of the coated edge. A new coating process based on vacuum arc PVD was developed to overcome this limitation. The film growth at the edges can be properly controlled by means of selected coating materials and process conditions. Thus, it is possible to grow edges sharper than the initial edge geometry. Different substrates were coated with different coating systems. Parameters such as the bias voltage, coating pressure, and initial radius were varied within this work. It was found that the application of a bias voltage is crucial for the generation of sharp edges. It was also found that the edge sharpening caused by coatings works best on samples with an initial radius of around 15 µm. [ABSTRACT FROM AUTHOR]

The complex working environment of mine leads to the degradation of the infrared image. The existing infrared image enhancement algorithm is easy to lose the scene details or causes the target edge blur while improving the signal-to-noise ratio and contrast. In order to solve the above problems, a mine infrared image enhancement algorithm based on dual domain decomposition coupling improved Gaussian Laplacian (ILoG) factor and contrast limited adaptive histogram equalization (CLAHE) (ILoG-CLAHE) is proposed. Firstly, the dual domain decomposition model is used to decompose the mine infrared image into a detailed sub-images containing high-frequency information and a basic sub-images containing low-frequency information. Secondly, the CLAHE algorithm is used to improve the brightness, contrast and definition of the basic sub-images to highlight the general features of the monitoring scene. The constructed ILoG operator is used to suppress noise and sharpen edges of detail sub-images and eliminate gradient inversion. Thirdly, the reconstructed image with improved image quality is obtained through the basic sub-image and detail sub-image after reconstruction processing. Finally, a Gamma correction function of gray level redistribution is designed to adjust the brightness of the reconstructed image. The mine infrared-enhanced image is obtained. The performance of the algorithm is analyzed by subjective vision and objective indicators. The results show that the overall visual effect and objective index of the mine infrared image enhanced by the mine infrared image enhancement algorithm based on dual domain and ILoG-CLAHE have been greatly improved. The comprehensive enhancement performance and robustness are better. Compared with the original mine infrared image and the six comparison algorithms, the comprehensive evaluation index values of this algorithm are increased by 0.28, 0.11, 0.23, 0.38, 0.57, 0.04, and 0.10 respectively. The six algorithms include CLAHE algorithm, bilateral filtering(BF) decomposition and CLAHE enhancement of basic sub-images (BF-CLAHE) algorithm, BF decomposition and Gamma transform (BF-Gamma) algorithm, guided filtering and Gamma transform (GF-Gamma) algorithm, adaptive histogram equalization(AHE) coupled Laplacian transform (AHE-LP) algorithm, and un-sharp mask(UM) based layer fusion (LF-UM) algorithm. The brightness, clarity and contrast of images are greatly improved, and noise suppression and edge sharpening are realized. It shows that the algorithm is suitable for the enhancement of infrared images in the complex working environment of mine.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Manually classifying and evaluating anything is difficult. It is difficult to manually count ripe fruits and evaluate their quality. Increasing labor costs, a shortage of skilled workers, and declining storage costs are just some of the major challenges associated with fruit production, marketing, and storage, among others. An effective method for localizing all clearly visible objects or portion of an object from an image has been proposed in this study, requiring less memory and processing resources. The main obstacles for object detection, such as object overlap, background noise, low resolution, etc, that prevents us from obtaining better results has been overcome by processing every input image. It also built an enhanced classification or recognition algorithm based on convolutional neural networks, which has shown to perform better than baseline studies. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) imaging was confirmed as one of the important evolving radiological features of disease diagnosis. In this work, we propose an image filtering method for CT images that fuses the spatial and the transform domain. Filters in the transform domain work well in restoring low-contrast details that are usually smoothed by spatial domain filters. The new filter is based on a modified anisotropic diffusion approach combined with the phase congruency (PC) feature. The PC feature is a scale-invariant compared to the classical gradient, where weak edges are usually omitted and undetected with gradient-based feature detectors. This feature is incorporated in the diffusion function to enhance image edges while eliminating noise and texture background. In a further step, a U-net convolutional network is used to segment the lung-infected area. Comparative tests and results show the efficiency of our method, which permits to enhance image features while removing noise perfectly. [ABSTRACT FROM AUTHOR]

In this study, an enhancement process obtained by applying the heat conduction equation of solid and stagnant fluids on colour images is proposed. After the colour channel stretching, the RGB colour image was converted to the HSI model. The heat conduction equation was applied for each pixel on the I channel of the HSI colour model. The elements of the feature matrix called heat conduction matrix (HCM) can have negative, positive or zero values. A pixel with a small negative HCM value indicates that I needs level enhancement for a good image, whereas a small positive HCM value means that the I level value will be reduced and aligned with its neighbours. High positive or negative values are defined as the edges of the objects and the I level values of such pixels are not changed to protect the edges. In addition, whether HCM is negative or positive, the balanced increment and decrement path at a level I ensures that the mean brightness value performs natural protection. Finally, an enhanced image is obtained by transitioning from the HSI to the RGB colour model. Experimental results show that this method can enhance colour image details better than other methods. [ABSTRACT FROM AUTHOR]

Copyright of Laser Technology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work we develop a post-processing algorithm which enhances the results of the existing image deblurring methods. It performs additional edge sharpening using grid warping. The idea of the proposed algorithm is to transform the neighborhood of the edge so that the neighboring pixels move closer to the edge, and then resample the image from the warped grid to the original uniform grid. The proposed technique preserves image textures while making the edges sharper. The effectiveness of the method is shown for basic deblurring methods on LIVE database images with added blur and noise. [ABSTRACT FROM AUTHOR]

In this study, the authors have proposed a new super resolution (SR) model based on the Perona–Malik regularisation scheme. The new model integrates into its regularisation component an adaptive exponential term which automatically adjusts itself depending on the local image features. This lends more sensitivity and adaptability to the proposed model, thereby making the reconstruction process much less punishing against semantically important features. Therefore, regularisation is stronger in homogeneous regions, and weaker in the neighbourhood of boundaries. The proposed method has a promising capability of supressing noise more effectively, while preserving important image features. The approach used differs significantly from the available methods, especially in the manner in which adaptability has been deployed. Noting that SR methods are less sensitive to the local image topography, a factor that causes the super‐resolved images to be visually poor, the new method sensitively probes the local features of the image, and determines the necessary level of reconstruction and regularisation. Additionally, the formulation robustly introduces a backward diffusion, a phenomenon proved from literature to have a tendency of sharpening edges. The authors have included empirical reconstruction results to demonstrate that their model produces better images in comparison with other classical methods. [ABSTRACT FROM AUTHOR]

MR imaging is an emerging and fast growing medical imaging technique which gives high quality images of the soft tissues. There are certain kinds of noise which contaminates these images and thus makes their interpretation difficult for both human and machine. Filtering is a mathematical technique in which intensities of each pixel of the input image are combined with the intensities of its neighboring pixels, to remove the noise and smooth the image. Filtering could be used with MR images for noise removal. The ordinary image filters blur the image and also remove important structural information like lines and edges. This loss of structural information could be dangerous in a clinical environment and could leads to incorrect diagnosis. To address this problem, a structure preserving noise filter is required. When images are processed for human vision, it is also desirable to make them pleasing by sharpening their edges. Such a structure adaptive noise (SAN) filter and an edge sharpening method is designed and implemented on our MRI Visualization toolkit. Our results show that the methods are effective in removing noise, preserving structure, and sharpening edges. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a fuzzy bidirectional flow framework based on generalized fuzzy set is presented to sharpen image by reducing its edge width, which performs a fuzzy backward (inverse) diffusion along the gradient direction to the isophote line (edge), while does a certain forward diffusion along the tangent direction on the contrary. Gaussian smoothing to the second normal derivative of an image is used to decide its zero-crossing, which results in a robust sharpening process against noise. Controlled by the image gradient magnitude, the fuzzy membership function guarantees a natural transition across different areas. To preserve image features, the nonlinear diffusion coefficients are locally adjusted according to the directional derivatives of the image. Experiments on real images demonstrate that the algorithm substantially improves the visual quality of the enhanced image over some relevant equations. [Copyright &y& Elsevier]

Nonlinear transmission lines (NLTL) are used for pulse shaping. We developed the theory of pulse propagation through the NLTL. The problem of a wide pulse degenerating into multiple pulses rather than a single pulse is solved by using a gradually scaled NLTL. We exploit certain favorable properties of accumulation-mode MOS varactors to design an NLTL that can simultaneously sharpen both rising and falling edges. There is a good agreement among the theory, simulations, and measurements. [ABSTRACT FROM AUTHOR]

The principles of Gradient operator, Laplacian operator, LOG operator and Sobel operator are discussed. Certain features of breast can be extracted in different degrees and aspects from original images by applying four edge sharpening operators to the breast near-infrared imaging. A great number of cases prove that compared with the other three operators, the improved Sobel operator can effectively extract the structural features of the breast from an original image. It can be concluded that the improved Sobel operator can assist in diagnosing breast diseases. [ABSTRACT FROM AUTHOR]

Anisotropic microstructures in ceramic green bodies result in nonuniform dimensional changes during sintering. Quantitative characterization of particle orientation together with processing/orientation relationships in these ceramic components are essential to control the dimensional variations during sintering. In this report, we discuss some practical aspects of measuring edge orientation in microstructures obtained by Field Emission Scanning Electron Microcscopy. Edges in the scanning direction were found to be less sensitive to the edge detection operator. Various edge sharpening algorithms were used to improve the edge contrast and hence achieve unbiased edge detection. Edges were detected using the Laplacian of Gaussian operator. Edge orientation polargrams were obtained by computing the intensities of detected edges with a 5×5 Prewitts operator convolved with a Gaussian. The computed edge orientation polargrams from the edge sharpened images were found to be internally consistent. Quantitative measure of particle orientation in tape cast ceramic microstructures was used for the first time to explain nonuniform sintering shrinkages. [ABSTRACT FROM AUTHOR]