bicubic interpolation results

[lilygeorgescu]

Hi,

I computed the PSNR and SSIM values using your code (common/evaluate.py) on the fastMRI dataset (on the test split provided by you) using the singlecoil images and I obtained the following results:

Bicubic interpolation method on PDWI (x2): PSNR = 37.1 +/- 4.023 SSIM = 0.8996 +/- 0.07312 Bicubic interpolation method on FS-PDWI (x2): PSNR = 31.86 +/- 4.345 SSIM = 0.728 +/- 0.1362

These numbers are very different from those reported in your paper. Did you use any other pre-processing step before applying bicubic interpolation? Or did you use other evaluation protocol?

Thank you, Best regards, Lili Georgescu

[chunmeifeng]

Could you please carefully check your code? I think there would be something wrong in the code

lilygeorgescu @.***> 于2022年1月28日周五 16:05写道：

Hi,

I computed the PSNR and SSIM values using your code (common/evaluate.py) on the fastMRI dataset (on the test split provided by you) using the singlecoil images and I obtained the following results:

Bicubic interpolation method on PDWI (x2): PSNR = 37.1 +/- 4.023 SSIM = 0.8996 +/- 0.07312 Bicubic interpolation method on FS-PDWI (x2): PSNR = 31.86 +/- 4.345 SSIM = 0.728 +/- 0.1362

These numbers are very different from those reported in your paper. Did you use any other pre-processing step before applying bicubic interpolation? Or did you use other evaluation protocol?

Thank you, Best regards, Lili Georgescu

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[lilygeorgescu]

Hi,

First, I want to thank you for taking the time to answer me. I really appreciate it.

I used the code from your repo (common/evaluate.py), and I replaced the loading of the reconstruction images:

so instead of

recons = recons['reconstruction'][()]

I did this:

recons = resize(target.copy())

def resize(images):
    new_images = []
    for image in images:
        h, w = image.shape

        res_image = cv.resize(
            cv.resize(image, None, fx=0.5, fy=0.5, interpolation=cv.INTER_CUBIC),
            (w, h),  interpolation=cv.INTER_CUBIC)

        new_images.append(res_image)

    return np.array(new_images)

The resize function downsamples the image using bicubic interpolation, then it upsamples the image back to its original same (also using bicubic).

Did you do anything different when computing the results for bicubic interpolation?

I used the images from the singlecoil folder downloaded from the original website.

Thank you, Best regards, Lili Georgescu

[chunmeifeng]

sorry for the late reply, I think there are something wrong in your code, because many readers has already repeated the results.

lilygeorgescu @.***> 于2022年1月29日周六 18:44写道：

Hi,

First, I want to thank you for taking the time to answer me. I really appreciate it.

I used the code from your repo (common/evaluate.py), and I replaced the loading of the reconstruction images:

so instead of

recons = recons['reconstruction'][()]

I did this:

recons = resize(target.copy())

def resize(images): new_images = [] for image in images: h, w = image.shape

    res_image = cv.resize(
        cv.resize(image, None, fx=0.5, fy=0.5, interpolation=cv.INTER_CUBIC),
        (w, h),  interpolation=cv.INTER_CUBIC)

    new_images.append(res_image)

return np.array(new_images)

The resize function downsamples the image using bicubic interpolation, then it upsamples the image back to its original same (also using bicubic).

Did you do anything different when computing the results for bicubic interpolation?

I used the images from the singlecoil folder downloaded from the original website.

Thank you, Best regards, Lili Georgescu

— Reply to this email directly, view it on GitHub https://github.com/chunmeifeng/MINet/issues/4#issuecomment-1024955687, or unsubscribe https://github.com/notifications/unsubscribe-auth/AR75XN2375WNMKGS3HVBASTUYQRPXANCNFSM5NA7Q7CA . Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

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