A New Image Interpolation Using Laplacian Operator

In this paper, a novel method for image interpolation is proposed. This method is based on the application of the Laplacian operator for the purpose of detecting the edge-directions and then interpolating the missing pixels using the cubic convolution. We start applying a down-sampled by a factor of two to the gray high-resolution image in order to obtain a low-resolution image. Then, the preprocessed image is reconstructed by using the proposed interpolation method. The proposed method is implemented and tested over several gray images, and also compared to many interpolation methods in the state-of-the-art. The simulation results are shown to be superior compared to the other interpolation methods in both of objective measurement in terms of PSNR, SSIM and FSIM, and visual quality of image results.

[1]  Truong Q. Nguyen,et al.  Markov Random Field Model-Based Edge-Directed Image Interpolation , 2007, IEEE Transactions on Image Processing.

[2]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[3]  Nicola Asuni,et al.  Accuracy Improvements and Artifacts Removal in Edge Based Image Interpolation , 2008, VISAPP.

[4]  Li Hong,et al.  An adaptive algorithm for image resolution enhancement , 2000, Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154).

[5]  Sheila S. Hemami,et al.  Regularity-preserving image interpolation , 1999, IEEE Trans. Image Process..

[6]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[7]  Seongjai Kim,et al.  The Error-Amended Sharp Edge (EASE) Scheme for Image Zooming , 2007, IEEE Transactions on Image Processing.

[8]  Ping Wah Wong,et al.  Edge-directed interpolation , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[9]  Hwang Soo Lee,et al.  Adaptive image interpolation based on local gradient features , 2004, IEEE Signal Process. Lett..

[10]  R. Keys.  Cubic convolution interpolation for digital image processing , 1981 .

[11]  Mei-Juan Chen,et al.  A fast edge-oriented algorithm for image interpolation , 2005, Image Vis. Comput..

[12]  Lei Zhang,et al.  An edge-guided image interpolation algorithm via directional filtering and data fusion , 2006, IEEE Transactions on Image Processing.

[13]  K. Adamczyk,et al.  Application of 2D Anisotropic Wavelet Edge Extractors for Image Interpolation , 2012 .

[14]  Ron Kimmel,et al.  Demosaicing: Image Reconstruction from Color CCD Samples , 1998, ECCV.

[15]  Nicola Asuni,et al.  Fast Artifacts-Free Image Interpolation , 2008, BMVC.

[16]  Giovanni Ramponi,et al.  Warped distance for space-variant linear image interpolation , 1999, IEEE Trans. Image Process..

[17]  Thomas W. Parks,et al.  Prediction of image detail , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[18]  Weiming Dong,et al.  Image zooming using directional cubic convolution interpolation , 2012 .

[19]  Dimitris Anastassiou,et al.  Subpixel edge localization and the interpolation of still images , 1995, IEEE Trans. Image Process..