Image Interpolation by Pixel‐Level Data‐Dependent Triangulation

We present a novel image interpolation algorithm. The algorithm can be used in arbitrary resolution enhancement, arbitrary rotation and other applications of still images in continuous space. High‐resolution images are interpolated from the pixel‐level data‐dependent triangulation of lower‐resolution images. It is simpler than other methods and is adaptable to a variety of image manipulations. Experimental results show that the new “mesh image” algorithm is as fast as the bilinear interpolation method. We assess the interpolated images' quality visually and also by the MSE measure which shows our method generates results comparable in quality to slower established methods. We also implement our method in graphics card hardware using OpenGL which leads to real‐time high‐quality image reconstruction. These features give it the potential to be used in gaming and image‐processing applications.

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

[2]  Larry L. Schumaker,et al.  Cubic spline fitting using data dependent triangulations , 1990, Comput. Aided Geom. Des..

[3]  S. Rippa,et al.  Data Dependent Triangulations for Piecewise Linear Interpolation , 1990 .

[4]  Anastasios N. Venetsanopoulos,et al.  Image interpolation based on variational principles , 1991, Signal Process..

[5]  Michael Unser,et al.  Fast B-spline Transforms for Continuous Image Representation and Interpolation , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Scott J. Daly,et al.  Visible differences predictor: an algorithm for the assessment of image fidelity , 1992, Electronic Imaging.

[7]  B. Ayazifar,et al.  PEL-adaptive model-based interpolation of spatially subsampled images , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  D C Van Essen,et al.  Information processing in the primate visual system: an integrated systems perspective. , 1992, Science.

[9]  Mikio Takagi,et al.  High-quality image magnification applying the gerchberg-papoulis iterative algorithm with DCT , 1994, Systems and Computers in Japan.

[10]  Robert L. Stevenson,et al.  A Bayesian approach to image expansion for improved definitio , 1994, IEEE Trans. Image Process..

[11]  Russell M. Mersereau,et al.  A new method for directional image interpolation , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

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

[13]  Arun N. Netravali,et al.  Digital Pictures: Representation, Compression and Standards , 1995 .

[14]  S.A. Martucci.  Image resizing in the discrete cosine transform domain , 1995, Proceedings., International Conference on Image Processing.

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

[16]  Bryan S. Morse,et al.  Isophote-based interpolation , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[17]  Narendra Ahuja,et al.  POCS based adaptive image magnification , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[18]  Thomas Martin Deserno,et al.  Survey: interpolation methods in medical image processing , 1999, IEEE Transactions on Medical Imaging.

[19]  Wilson S. Geisler,et al.  Image quality assessment based on a degradation model , 2000, IEEE Trans. Image Process..

[20]  Bryan S. Morse,et al.  Image magnification using level-set reconstruction , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[21]  Thomas W. Sederberg,et al.  Image Reconstruction Using Data-Dependent Triangulation , 2001, IEEE Computer Graphics and Applications.

[22]  M. Orchard,et al.  New edge-directed interpolation , 2001, IEEE Trans. Image Process..

[23]  Sebastiano Battiato,et al.  A locally adaptive zooming algorithm for digital images , 2002, Image Vis. Comput..

[24]  Philip J. Willis,et al.  Demosaicing of color images using pixel level data-dependent triangulation , 2003, Proceedings of Theory and Practice of Computer Graphics, 2003..