Image super-resolution based on deep neural network of multiple attention mechanism

Author(s):  
Xin Yang ◽  
Xiaochuan Li ◽  
Zhiqiang Li ◽  
Dake Zhou
2020 ◽  
Vol 34 (07) ◽  
pp. 11807-11814
Author(s):  
Jinshan Pan ◽  
Yang Liu ◽  
Deqing Sun ◽  
Jimmy Ren ◽  
Ming-Ming Cheng ◽  
...  

We present a simple and effective image super-resolution algorithm that imposes an image formation constraint on the deep neural networks via pixel substitution. The proposed algorithm first uses a deep neural network to estimate intermediate high-resolution images, blurs the intermediate images using known blur kernels, and then substitutes values of the pixels at the un-decimated positions with those of the corresponding pixels from the low-resolution images. The output of the pixel substitution process strictly satisfies the image formation model and is further refined by the same deep neural network in a cascaded manner. The proposed framework is trained in an end-to-end fashion and can work with existing feed-forward deep neural networks for super-resolution and converges fast in practice. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods.


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