From ce9a8ef7bd3850eb2688b0cd9530bd5fb91524c3 Mon Sep 17 00:00:00 2001 From: Aaron Defazio Date: Thu, 1 Sep 2022 11:19:06 -0400 Subject: [PATCH] Removed dead code (#268) --- banding_removal/fastmri/data/transforms.py | 25 ---------------------- 1 file changed, 25 deletions(-) diff --git a/banding_removal/fastmri/data/transforms.py b/banding_removal/fastmri/data/transforms.py index da1222a0..7e42dfc8 100644 --- a/banding_removal/fastmri/data/transforms.py +++ b/banding_removal/fastmri/data/transforms.py @@ -488,31 +488,6 @@ def complex_planar_to_packed(data): imaginary = data[..., 1, :, :] return torch.stack([real, imaginary], dim=-1) - -def complex_whiten(complex_image, eps=1e-10): - real = complex_image[:, :, 0] - imag = complex_image[:, :, 1] - - # Center around mean. - centered_complex_image = complex_image - complex_image.mean() - - # Determine covariance between real and imaginary. - n = real.nelement() - real_real = (real.mul(real).sum() - real.mean().mul(real.mean())) / n - real_imag = (real.mul(imag).sum() - real.mean().mul(imag.mean())) / n - imag_imag = (imag.mul(imag).sum() - imag.mean().mul(imag.mean())) / n - V = torch.Tensor([[real_real, real_imag], [real_imag, imag_imag]]) - - # Remove correlation by rotating around covariance eigenvectors. - eig_values, eig_vecs = torch.eig(V, eigenvectors=True) - whitened_image = torch.matmul(centered_complex_image, eig_vecs) - - # Scale by eigenvalues for unit variance. - whitened_image[:, :, 0] = whitened_image[:, :, 0] / (eig_values[0, 0] + eps).sqrt() - whitened_image[:, :, 1] = whitened_image[:, :, 1] / (eig_values[1, 0] + eps).sqrt() - return whitened_image - - # Helper functions def roll(x, shift, dim):