Statistically Matched Wavelet Based Texture Synthesis in a Compressive Sensing Framework

Algorithm 1

CS measurement and encoding.

(1)  Design a 2-D separable kernel filter bank using Statistically Matched Wavelet [Detailed in Section 2]

  (a)  Decompose input image into approximation and detail subbands coefficients

    (i)  Use Wavelet function from MATLAB “wavecut” to represent approximation and detail

     coefficients

(2)  Design a CS measurement matrix Φ using Noiselet Transform [47] and detail subbands coefficients

(3)  Do quantization of the CS measurements using (7)

(4)  Do Entropy coding of the quantized CS measurements

(5)  Combine standard and CS encoded bit streams