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Edge Rate and Magnification RMSE Correlation

Edge Rate and Magnification RMSE Correlation

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Different image magnification methods are related very close with each other except the simplest Box method that does not use any interpolation. All tested methods show about the same RMSE results for the same picture. Lanczos methods show the best results and simplest Box method shows the worst results. Statistically, difference between interpolat...

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Context 1
... is evidence, as shown in Fig. 1, that there is no clear relation between Edge Rate Index and image RMSE index. From the same data correlation shown in Table 1 was calculated. Table 1 shows that there is weak relation between Edge Rate and RMSE of different magnification methods, but there is strong relation between different magnification methods. ...
Context 2
... the same data correlation shown in Table 1 was calculated. Table 1 shows that there is weak relation between Edge Rate and RMSE of different magnification methods, but there is strong relation between different magnification methods. Actually when there are two images with different RMSE, for one method, the image with lower RMSE yields the lower RMSE result with any other magnification method. ...

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Image magnification has a wide range of application, including the uploading of images to a web page, display of images in mobile phones, PDAs or screens. Due to the limited memory in these devices, the need to use simple image magnification algorithms arises. Mathematical morphology based on algebraic framework endows it with strong properties and allows multiple extensions for image magnification. In specific, extensions to fuzzy sets using morphological operators namely, dilation and erosion are performed while preserving the properties of these operators. This paper utilizes this approach that helps in magnifying the images, called, Interval-valued Fuzzy Lattice Morphology-based Image Transformation (IFLM-IT). The IFLM-IT method obtains as input an original colour RGB image using Cartesian Co-ordinate system. Next, Fuzzy Lattice Morphology-based Image Transformation is applied to reduce original colour RGB image to smaller size aiming at attaining better quality image. In this work, we associate Interval-valued Fuzzy Sets to magnify transformed image to original size. Based on this set, the magnified image is compared with the original colour image. We show certain experimental results and study how the Interval-valued Fuzzy Lattice Morphology-based Image Transformation has influence on the results obtained by the algorithm. Comprehensive evaluations on a large dataset well demonstrate the better performance of IFLM-IT method over other state-of-the-arts for image magnification.