Unlike the blocked transform‐based image processing, the wavelet transform operates on the image as a whole but still has the same computational complexity. This chapter concentrates on the application of Haar wavelet for image interpolation, as it is the easiest to work with and can still provide very good interpolation results. A natural way to exploit the discrete wavelet transform (DWT) multi‐resolution analysis property is to make use of the multi‐resolution dependency between different levels of wavelet decomposition to estimate the high frequency subband images required for the inverse transform to create the interpolated image. The regularity‐preserving interpolation technique synthesizes a new wavelet subband based on the decay of known wavelet transform coefficients. Although wavelet interpolation algorithm is not block‐based interpolation algorithm, blocking‐like artifacts (the zigzag artifacts) are observed in the interpolated image. This is caused by the incomparability between the low frequency subband image and the high frequency subband images.
Wavelet
Digital Image Interpolation in MATLAB® ; 161-190
2019-02-19
30 pages
Article/Chapter (Book)
Electronic Resource
English
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