Image Formation Model Guided Deep Image Super-Resolution

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.

Authors

• Jinshan Pan Nanjing University of Science and Technology, China
• Yang Liu College of Computer Science, Sichuan University Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education
• Deqing Sun Google
• Jimmy Ren SenseTime Research
• Ming-Ming Cheng Nankai University NKIARI
• Jian Yang
• Jinhui Tang Nanjing University of Science and Technology

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