Implementing compressive fractional Fourier transformation with iterative kernel steering regression in double random phase encoding

This paper proposes a novel approach in double random phase encryption based on compressive fractional Fourier transform along with the kernel steering regression. The method increases the complexity of the image by using fractional Fourier transform and taking fewer measurements from the image data. Numerical results are given to analyze the validity of this technique. Considering natural images to be sparse in some domain, we apply a compressive sensing (CS) approach by using a TwIST algorithm. The encryption process has kernel steering regression algorithm for denoising and compressive sensing technique for image compression along with the fractional Fourier transform that makes the image in more complex form.