Characteristic Regularisation for Super-Resolving Face Images
This addresses the domain gap problem in facial image super-resolution for applications like surveillance or forensics, offering an incremental improvement over existing unsupervised domain adaptation methods.
The paper tackles the performance drop of facial image super-resolution models on genuine low-resolution data by introducing Characteristic Regularisation, which separates and controls optimisations for characteristics consistifying and image super-resolving, leading to superior performance over state-of-the-art models on both genuine and artificial data.
Existing facial image super-resolution (SR) methods focus mostly on improving artificially down-sampled low-resolution (LR) imagery. Such SR models, although strong at handling artificial LR images, often suffer from significant performance drop on genuine LR test data. Previous unsupervised domain adaptation (UDA) methods address this issue by training a model using unpaired genuine LR and HR data as well as cycle consistency loss formulation. However, this renders the model overstretched with two tasks: consistifying the visual characteristics and enhancing the image resolution. Importantly, this makes the end-to-end model training ineffective due to the difficulty of back-propagating gradients through two concatenated CNNs. To solve this problem, we formulate a method that joins the advantages of conventional SR and UDA models. Specifically, we separate and control the optimisations for characteristics consistifying and image super-resolving by introducing Characteristic Regularisation (CR) between them. This task split makes the model training more effective and computationally tractable. Extensive evaluations demonstrate the performance superiority of our method over state-of-the-art SR and UDA models on both genuine and artificial LR facial imagery data.