Image Formation Model Guided Deep Image Super-Resolution
This work addresses image quality enhancement for computer vision applications, but it is incremental as it builds on existing deep neural networks.
The authors tackled image super-resolution by integrating an image formation constraint into deep neural networks via pixel substitution, achieving favorable performance against state-of-the-art methods.
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.