Abstract
This paper presents an adaptive anti-aliasing algorithm based on the wavelet-Fourier transform and directionally adaptive wavelet shrinkage. The proposed antialiasing algorithm detects aliasing artifacts by analyzing the frequency characteristics of discrete wavelet transform (DWT) coefficients, and then removes the aliasing artifacts by shrinking the transform coefficients in the directionally adaptive manner. More specifically, the proposed algorithm analyzes the property of LH, HL, and HH subbands of the DWT, and reduces aliasing artifacts in the LL subband by shrinking the coefficients in the patch-based adaptive manner. On the other hand, aliasing artifacts in LH, HL, and HH subbands are reduced using a directional filtering. The resulting anti-aliased image is obtained by taking the inverse DWT. Experimental results show that the proposed algorithm can efficiently reduce the aliasing artifacts while preserving the high-frequency image details. The proposed anti-aliasing algorithm is suitable for not only mobile imaging systems but also various application areas of image restoration and multidimensional sampling.
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