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Deep Residual Learning for Image Restoration(SISR/Denoising) : Persistent Homology-Guided Manifold Simplification

MATLAB 29.69% Makefile 0.98% TeX 8.39% Python 5.56% CSS 0.34% JavaScript 0.04% HTML 0.44% C++ 17.05% Cuda 23.68% C 1.42% Shell 1.72% Protocol Buffer 10.69%

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codeaudit liuguoyou yangman111 taixiangjiang mengjialiu dreamchasermxf zhiqiangone stiflerbox karaogluhh hanyoseob

cnn's Issues

SISR

(CVPRW 2017)' to other places due to capacity，can you give me address,I do not find it

Training loss, training code

Hi,

I haven't found an email address to directly contact you, hence I am writing on Github :)

I have a question regarding the training loss. Is it defined as the L2 loss on the wavelet coefficients or is it defined in image space (ie by reconstructing the clean image from the predicted wavelet coefficients and then calculating the squared error to the ground truth)?

Is it possible to provide code for training your network?

Thanks and best regards,
Tobias Plötz

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