Cornsweet surfaces for selective contrast enhancement

A typical goal when enhancing the contrast of images is to increase the perceived contrast without altering the original feel of the image. Such contrast enhancement can be achieved by modelling Cornsweet profiles into the image. We demonstrate that previous methods aiming to model Cornsweet profiles for contrast enhancement, often employing the unsharp mask operator, are not robust to image content. To achieve robustness, we propose a fundamentally different vector-centric approach with Cornsweet surfaces. Cornsweet surfaces are parametrised 3D surfaces (2D in space, 1D in luminance enhancement) that are extruded or depressed in the luminance dimension to create countershading that respects image structure. In contrast to previous methods, our method is robust against the topology of the edges to be enhanced and the relative luminance across those edges. In user trials, our solution was significantly preferred over the most related contrast enhancement method. © 2014 Elsevier Ltd.

Cornsweet surfaces for selective contrast enhancement

A typical goal when enhancing the contrast of images is to increase the perceived contrast without altering the original feel of the image. Such contrast enhancement can be achieved by modelling Cornsweet profiles into the image. We demonstrate that previous methods aiming to model Cornsweet profiles for contrast enhancement, often employing the unsharp mask operator, are not robust to image content. To achieve robustness, we propose a fundamentally different vector-centric approach with Cornsweet surfaces. Cornsweet surfaces are parametrised 3D surfaces (2D in space, 1D in luminance enhancement) that are extruded or depressed in the luminance dimension to create countershading that respects image structure. In contrast to previous methods, our method is robust against the topology of the edges to be enhanced and the relative luminance across those edges. In user trials, our solution was significantly preferred over the most related contrast enhancement method. © 2014 Elsevier Ltd.

A Novel deep learning approach for the automated diagnosis of normal pressure hydrocephalus

Normal Pressure Hydrocephalus (NPH), an Atypical Parkinsonian syndrome, is a neurological syndrome that mainly affects elderly people. This syndrome shows the symptoms of Parkinson’s disease (PD), such as walking impairment, dementia, impaired bladder control, and mental impairment. The Magnetic Resonance Imaging (MRI) is the aptest modality for the detection of the abnormal build-up of cerebrospinal fluid in the brain’s cavities or ventricles, which is the major cause of NPH. This work aims to develop an automated biomarker for NPH segmentation and classification (NPH-SC) that efficiently detect hydrocephalus using a deep learning-based approach. Removal of non-cerebral tissues (skull, scalp, and dura) and noise from brain images by skull stripping, unsharp-mask based edge sharpening, segmentation by marker-based watershed algorithm, and labelling are performed to improve the accuracy of the CNN based classification system. The brain ventricles are extracted using the external and internal markers and then fed into the convolutional neural networks (CNN) for classification. This automated NPH-SC model achieved a sensitivity of 96%, a specificity of 100%, and a validation accuracy of 97%. The prediction system, with the help of a CNN classifier, is used for the calculation of test accuracy of the system and obtained promising 98% accuracy.

Cornsweet surfaces for selective contrast enhancement

A typical goal when enhancing the contrast of images is to increase the perceived contrast without altering the original feel of the image. Such contrast enhancement can be achieved by modelling Cornsweet profiles into the image. We demonstrate that previous methods aiming to model Cornsweet profiles for contrast enhancement, often employing the unsharp mask operator, are not robust to image content. To achieve robustness, we propose a fundamentally different vector-centric approach with Cornsweet surfaces. Cornsweet surfaces are parametrised 3D surfaces (2D in space, 1D in luminance enhancement) that are extruded or depressed in the luminance dimension to create countershading that respects image structure. In contrast to previous methods, our method is robust against the topology of the edges to be enhanced and the relative luminance across those edges. In user trials, our solution was significantly preferred over the most related contrast enhancement method. © 2014 Elsevier Ltd.

Cornsweet surfaces for selective contrast enhancement

A typical goal when enhancing the contrast of images is to increase the perceived contrast without altering the original feel of the image. Such contrast enhancement can be achieved by modelling Cornsweet profiles into the image. We demonstrate that previous methods aiming to model Cornsweet profiles for contrast enhancement, often employing the unsharp mask operator, are not robust to image content. To achieve robustness, we propose a fundamentally different vector-centric approach with Cornsweet surfaces. Cornsweet surfaces are parametrised 3D surfaces (2D in space, 1D in luminance enhancement) that are extruded or depressed in the luminance dimension to create countershading that respects image structure. In contrast to previous methods, our method is robust against the topology of the edges to be enhanced and the relative luminance across those edges. In user trials, our solution was significantly preferred over the most related contrast enhancement method. © 2014 Elsevier Ltd.

PENAJAMAN CITRA REMOTE SENSING MENGGUNAKAN METODE FILTERING DI AREA PEMBUDIDAYAAN RUMPUT LAUT

: Indonesia has excellent natural potential for seaweed industry. To help farmers understand the harvest period, an application needs to be built. In this research, a preprocessing application for processing seaweed image processing is taken through remote sensing. Preprocessing is using the Unsharp Mask Filtering and Laplacian Filtering methods. The performace metrics results using the SSIM method show that Laplacian level 2 filtering method gives better result.

Automatic detection and extraction algorithm of coronal loops based on match filter and oriented directivity

ABSTRACT In this paper, an efficient algorithm is developed to automatically detect and extract coronal loops. First of all, in the algorithm, three characteristics associated with coronal loops are used to construct a match filter able to enhance the loops. Secondly, the method combining a high-pass filter (unsharp-mask enhancement) with a global threshold is used to further enhance and segment the loops. Thirdly, to extract every individual coronal loop and obtain their parameters (the 2D projected space coordinates and lengths) from the segmented loops, a clustering method of the pixels with approximate local direction and connected domain is further used. Fourthly, to evaluate the performance of the developed algorithm, images observed by the Transition Region and Coronal Explorer (TRACE), the Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) and the High-Resolution Coronal Imager (Hi-C) are used, and comparison experiments between the existing algorithms and the developed algorithm are performed. Finally, it is found that the developed algorithm is commensurate with the two most promising algorithms, oriented coronal curved loop tracing (OCCULT) and its improved version, OCCULT-2, in performance. Therefore, for scientific applications associated with coronal loops, the developed algorithm will be a powerful tool.

Perbaikan Tingkat Kekaburan Gambar Akibat Pembesaran Pada Hasil Creenshot Dengan Metode Unsharp Mask

Screenshot is a display image taken from a monitor screen such as computers, tablet PCs and smartphones. The image results from the screenshot have a low level of sharpness and smoothness. If this image is enlarged, the quality becomes low like blur. To improve the quality of the image so that it is not blurred when enlarged, then the process of sharpening and smoothing. This process will improve the quality of the image to be better. The image that is processed in this research is the image screenshot. Furthermore, the image is processed using digital image processing using Matlab software. The processing stages are crop, image enhancement and unsharp mask. The image of the image enhancement and unsharp mask results are collaborated to increase and increase the sharpness and smoothness of the image in the results of the screenshot image being tested. The results of testing on this method with better sharpness quality with a comparison of images using the mean square error of 0.0627.2404%. The image of the test results can be concluded that the value of the pixels sought has a larger image size than the original and has a resolution greater than the initial image and sharpness and smoothness so that the unsharp mask method can improve the sharpness and smoothness of the image. But the changes produced using the method have not been significant enough.