Volume 5, Issue 3, March 2014, Pages 215–221
Said OUSGUINE1, Fedwa ESSANNOUNI2, Leila ESSANNOUNI3, and Driss Aboutajdine4
1 LRIT Laboratory (Unit associated with the CNRST), URAC 29, Faculty of Science, University Mohammed V, Agdal, Rabat, Morocco
2 LRIT Laboratory (Unit associated with the CNRST), URAC 29, Faculty of Science, University Mohammed V, Agdal, Rabat, Morocco
3 LRIT Laboratory (Unit associated with the CNRST), URAC 29, Faculty of Science, University Mohammed V, Agdal, Rabat, Morocco
4 Faculty of Sciences, Mohamed V University, Rabat, Morocco
Original language: English
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Several factors can interfere in the quality of image such as aliasing, noise, artifact, and blurring, these factors can cause the degradation of image especially in edge regions. In order to reduce the effect of these factors, it is necessary to choose a robust interpolation method which can play important role of the reconstruction of the high-resolution image from its low-resolution counterpart, so as to preserving the edges and textures, increasing the resolution, and improving the image quality.
In this paper, a new image interpolation method is proposed using gradient orientation; in the first step, we estimate the edges directions for a missing pixel location using the gradient-orientation in horizontal and vertical directions. Then, in the second step we interpolate the missing pixels along the detected edge directions using a cubic spline interpolation.
We begin from a gray high-resolution image which is down-sampled by a factor of two, to obtain the low-resolution image, then; this image is reconstructed using the proposed algorithm. Our method is implemented and tested to several gray test images, and compared to other image interpolation methods. The simulation results show the effectiveness of the proposed technique using the PSNR and compared with the traditional interpolation techniques. The results showed that the proposed technique has higher accuracy, and can preserve the sharp edges and textures, and avoid the problems of blurring and the visual artifacts caused by the classical interpolation methods.
Author Keywords: Image interpolation, gradient orientation, edges direction, cubic spline interpolation, super-resolution.
Said OUSGUINE1, Fedwa ESSANNOUNI2, Leila ESSANNOUNI3, and Driss Aboutajdine4
1 LRIT Laboratory (Unit associated with the CNRST), URAC 29, Faculty of Science, University Mohammed V, Agdal, Rabat, Morocco
2 LRIT Laboratory (Unit associated with the CNRST), URAC 29, Faculty of Science, University Mohammed V, Agdal, Rabat, Morocco
3 LRIT Laboratory (Unit associated with the CNRST), URAC 29, Faculty of Science, University Mohammed V, Agdal, Rabat, Morocco
4 Faculty of Sciences, Mohamed V University, Rabat, Morocco
Original language: English
Copyright © 2014 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Several factors can interfere in the quality of image such as aliasing, noise, artifact, and blurring, these factors can cause the degradation of image especially in edge regions. In order to reduce the effect of these factors, it is necessary to choose a robust interpolation method which can play important role of the reconstruction of the high-resolution image from its low-resolution counterpart, so as to preserving the edges and textures, increasing the resolution, and improving the image quality.
In this paper, a new image interpolation method is proposed using gradient orientation; in the first step, we estimate the edges directions for a missing pixel location using the gradient-orientation in horizontal and vertical directions. Then, in the second step we interpolate the missing pixels along the detected edge directions using a cubic spline interpolation.
We begin from a gray high-resolution image which is down-sampled by a factor of two, to obtain the low-resolution image, then; this image is reconstructed using the proposed algorithm. Our method is implemented and tested to several gray test images, and compared to other image interpolation methods. The simulation results show the effectiveness of the proposed technique using the PSNR and compared with the traditional interpolation techniques. The results showed that the proposed technique has higher accuracy, and can preserve the sharp edges and textures, and avoid the problems of blurring and the visual artifacts caused by the classical interpolation methods.
Author Keywords: Image interpolation, gradient orientation, edges direction, cubic spline interpolation, super-resolution.
How to Cite this Article
Said OUSGUINE, Fedwa ESSANNOUNI, Leila ESSANNOUNI, and Driss Aboutajdine, “A New Image Interpolation Using Gradient-Orientation and Cubic Spline Interpolation,” International Journal of Innovation and Applied Studies, vol. 5, no. 3, pp. 215–221, March 2014.
