  Benefiting from the strong capabilities of deep CNNs for feature
representation and nonlinear mapping, deep-learning-based methods have achieved
excellent performance in single image super-resolution. However, most existing
SR methods depend on the high capacity of networks which is initially designed
for visual recognition, and rarely consider the initial intention of
super-resolution for detail fidelity. Aiming at pursuing this intention, there
are two challenging issues to be solved: (1) learning appropriate operators
which is adaptive to the diverse characteristics of smoothes and details; (2)
improving the ability of model to preserve the low-frequency smoothes and
reconstruct the high-frequency details. To solve them, we propose a purposeful
and interpretable detail-fidelity attention network to progressively process
these smoothes and details in divide-and-conquer manner, which is a novel and
specific prospect of image super-resolution for the purpose on improving the
detail fidelity, instead of blindly designing or employing the deep CNNs
architectures for merely feature representation in local receptive fields.
Particularly, we propose a Hessian filtering for interpretable feature
representation which is high-profile for detail inference, a dilated
encoder-decoder and a distribution alignment cell to improve the inferred
Hessian features in morphological manner and statistical manner respectively.
Extensive experiments demonstrate that the proposed methods achieve superior
performances over the state-of-the-art methods quantitatively and
qualitatively. Code is available at https://github.com/YuanfeiHuang/DeFiAN.
