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View Code? Open in Web Editor NEW[IEEE JSTARS] Official PyTorch Implementation of "SAR Image Despeckling Using Continuous Attention Module"
License: MIT License
[IEEE JSTARS] Official PyTorch Implementation of "SAR Image Despeckling Using Continuous Attention Module"
License: MIT License
Hello! I would like to ask, would you mind updating the preprocessing code for the dataset? For example, use MATLAB to add speckle noise. There is also the "cropping of the image into a 64 × 64 patch size" mentioned in the paper. Is it a random cropping of the original image? Does this mean that the training and validation set image sizes are 64 × 64?
Looking forward to your reply.
Hello, Author,
I successfully ran python train.py --project PROJECT_NAME --noisy-train-dir NOISY_IMAGE_TRAIN_DIR --clean-train-dir CLEAN_IMAGE_TRAIN_DIR --noisy-valid-dir NOISY_IMAGE_VALID_DIR --clean-valid-dir CLEAN_IMAGE_VALID_DIR and obtained the weight parameters SAR-CAM_best.pth. However, when I ran the second set of code for testing on the test set, I encountered a mismatch between the weights and the architecture when loading the weight parameters. The specific error code is:
RuntimeError: Error(s) in loading state_dict for SAR_CAM: Unexpected key(s) in state_dict: "conv_in.conv.bias", "conv_out.conv.bias", ... "up.up.1.conv_in.conv.bias", "up.bottleneck.conv.bias"...
I am not clear about the specific reason. I hope you can help to clarify. Thank you!
Could you please send me the adjusted data set? I would be very grateful.
Could you please describe the environment configuration and how to inference?
Dear author, it's a great honor to see your work, would you mind making your dataset public?
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