Zhaozixiang1228 / MMIF-CDDFuse

[CVPR 2023] Official implementation for "CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion."
349 stars 34 forks source link

Some minor issues #7

Open usherbraty opened 1 year ago

usherbraty commented 1 year ago
  1. In img_read_save.py, imsave(os.path.join(savepath, "{}.png".format(imagename)), image) should replace with imsave(os.path.join(savepath, "{}.png".format(imagename)), image.astype(np.uint8));
  2. In test_IVF.py and test_MIF.py, you should add from torch.nn.parallel import DataParallel;
  3. In README.md, the title of second output is wrong;
  4. In the chart of paper, parameter Qbaf should be Qabf.
Zhaozixiang1228 commented 1 year ago

Thank you very much for your attention to the project and thorough reading. Your suggestions are absolutely correct, and I will make improvements in the revised version.

123456gwp commented 1 year ago

Is there an incorrect subscript in Formula 5 of Section 3.3 in your paper? Whether V was mistakenly written as I.

Zhaozixiang1228 commented 1 year ago

@123456gwp Yes, you are right, I wrote it incorrectly.

windlu-1 commented 1 year ago

2. from torch.nn.parallel import DataParallel

imsave(os.path.join(savepath, "{}.png".format(imagename)), image.astype(np.uint8)); 由于使用了浮点数,所以定义SSIM时,需要加上data_range? data_range = image_F.max() - image_F.min() return ssim(image_F,image_A,data_range=data_range)+ssim(image_F,image_B,data_range=data_range)

Gunkill commented 7 months ago

请问M3FD数据集是如何划分训练集与验证集的呢