A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filters for common resampling tasks
Graphics gems
Image interpolation and resampling
Handbook of medical imaging
Reconstruction filters in computer-graphics
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Graphics Gems III
JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures
JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures
Image Processing - Principles and Applications
Image Processing - Principles and Applications
An edge-preserving image interpolation system for a digital camcorder
IEEE Transactions on Consumer Electronics
IEEE Transactions on Image Processing
New edge-directed interpolation
IEEE Transactions on Image Processing
Subpixel edge localization and the interpolation of still images
IEEE Transactions on Image Processing
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Image interpolation is an important image processing operation applied in diverse areas ranging from computer graphics, rendering, editing, medical image reconstruction, to online image viewing. Image interpolation techniques are referred in literature by many terminologies, such as image resizing, image resampling, digital zooming, image magnification or enhancement, etc. Basically, an image interpolation algorithm is used to convert an image from one resolution (dimension) to another resolution without loosing the visual content in the picture. Image interpolation algorithms can be grouped in two categories, non-adaptive and adaptive. The computational logic of an adaptive image interpolation technique is mostly dependent upon the intrinsic image features and contents of the input image whereas computational logic of a non-adaptive image interpolation technique is fixed irrespective of the input image features. In this paper, we review the progress of both non-adaptive and adaptive image interpolation techniques. We also proposed a new algorithm for image interpolation in discrete wavelet transform domain and shown its efficacy. We describe the underlying computational foundations of all these algorithms and their implementation techniques. We present some experimental results to show the impact of these algorithms in terms of image quality metrics and computational requirements for implementation.