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.Comment: AAAI 2020. The training code and models are available at
https://github.com/jspan/PHYSICS S
