Open JingyunLiang opened 4 years ago
Yes, this work is mainly to force the trained network to adapt to unseen spatial degenerations. Specificly, we utilize five different settings Gaussian kernel to generate the LR HSI for training and adopt four motion kernels to generate the test LR HSI. In supplementation of the main paper can find the estimated kernel.
Reply to exact quetions:
Thanks for your quick reply. Nice job! The generalizability to different kernels is amazing.
What do you think of the importance of the regularization of spectral response function, large channel number (31-dimensional data share the same kernel), and the design of the adaptation model? Did you try relevant ablation study?
Btw, where can I download the four kernels in Fig. 5 of the paper?
As shown in the below line https://github.com/JiangtaoNie/UAL/blob/5793e197ca05e7b989a650cf095e87c8a76a9940/Train_FusionModel.py#L38 It seems that only five different Gaussian kernels are used in training the fuse modules, but they are shown to generalize well to kernels in Fig. 5 of the paper.
1, How did you choose these five kernels? 2, Did you try other sampling, e.g. uniform sampling ? 3, Does it generalize to other arbitrary Gaussian /non-Gaussian kernels? 4, Are the estimated kernels in the degeneralization module similar to ground-truth ones? 5, The spectral downsampler is fixed. Does it also act as an extra regularization in Eq. 4 of the paper? 6, MHF-net and other works are not trained with noisy LR images. Did you try to train a noisy version for comparison?
Thank you.