A novel single image domain learning CNN based reconstruction method for phase-varied images is proposed in which real and imaginary part of complex image are reconstructed independently. Proposed method uses symmetrical sub-sampling which enable reconstruction for real and imaginary part of complex images independently of each other without estimating phase distribution on the image. Reconstruction experiments showed that higher PSNR images are obtained in proposed method compared to phase estimating CNN or ADMM-CSNet. Proposed method is highly practical since it is robust to phase variation and is easy for training because of its simple CNN structure
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