how did you get GraphAgg*

[qiqiing]

When you are proving ‘Effectiveness of Graph Aggregation Module’, how did you get GraphAgg*. I processed it like this: directly compare the input low-resolution image and its down-sampling form in the image domain to obtain idx_k, and then aggregate to obtain z_sr (using the GraphAggregation in your code) ). What is wrong with this approach? The PSNR of my aggregation result is lower than the result obtained by directly performing ‘bicubic’.

[sczhou]

Hi,
Compared with the image domain, vgg feature domain is still more robust for patching matching in GraphAgg*. In addition, the non-learning version GraphAgg* is heavily affected by the degree of patch recurrence in the input image. You could try to obtain idx_k in the feature domain and test on an image that contains some cross-scale recurrent patches.

[qiqiing]

Hi,
Compared with the image domain, vgg feature domain is still more robust for patching matching in GraphAgg*. In addition, the non-learning version GraphAgg* is heavily affected by the degree of patch recurrence in the input image. You could try to obtain idx_k in the feature domain and test on an image that contains some cross-scale recurrent patches.

Thank you, what you mean is to first input the input image and its down-sampled version into the pre-trained VGG19, look for idx_k in the feature domain, and then aggregate the image domain images according to the found idx_k (the original input and its low-resolution version ), is that correct?