In this article, fine perceptive generative adversarial networks (FP-GANs) are proposed to produce super-resolution (SR) MR images from the low-resolution counterparts.  By adopting the divide-and-conquer scheme, FP-GANs are designed to deal with the low-frequency (LF) and high-frequency (HF) components of MR images separately and parallelly.  Specifically, FP-GANs first decompose an MR image into LF global approximation and HF anatomical texture subbands in the wavelet domain.  Finally, the reconstruction of the whole image is considered by integrating inverse discrete wavelet transformation in FP-GANs.  Moreover, FP-GANs further show the value by applying the SR results in classification tasks.
FP-GANs
In this article, fine perceptive generative adversarial networks (FP-GANs) are proposed to produce super-resolution (SR) MR images from the low-resolution counterparts.  By adopting the divide-and-conquer scheme, FP-GANs are designed to deal with the low-frequency (LF) and high-frequency (HF) components of MR images separately and parallelly.  Specifically, FP-GANs first decompose an MR image into LF global approximation and HF anatomical texture subbands in the wavelet domain.  Finally, the reconstruction of the whole image is considered by integrating inverse discrete wavelet transformation in FP-GANs.  Moreover, FP-GANs further show the value by applying the SR results in classification tasks.
FP-GANs
In this article, fine perceptive generative adversarial networks (FP-GANs) are proposed to produce super-resolution (SR) MR images from the low-resolution counterparts.  By adopting the divide-and-conquer scheme, FP-GANs are designed to deal with the low-frequency (LF) and high-frequency (HF) components of MR images separately and parallelly.  Specifically, FP-GANs first decompose an MR image into LF global approximation and HF anatomical texture subbands in the wavelet domain.  Finally, the reconstruction of the whole image is considered by integrating inverse discrete wavelet transformation in FP-GANs.  Moreover, FP-GANs further show the value by applying the SR results in classification tasks.
FP-GANs
In this article, fine perceptive generative adversarial networks (FP-GANs) are proposed to produce super-resolution (SR) MR images from the low-resolution counterparts.  By adopting the divide-and-conquer scheme, FP-GANs are designed to deal with the low-frequency (LF) and high-frequency (HF) components of MR images separately and parallelly.  Specifically, FP-GANs first decompose an MR image into LF global approximation and HF anatomical texture subbands in the wavelet domain.  Finally, the reconstruction of the whole image is considered by integrating inverse discrete wavelet transformation in FP-GANs.  Moreover, FP-GANs further show the value by applying the SR results in classification tasks.
FP-GANs