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Liquify is a common technique for image editing, which can be used for imagedistortion. Due to the uncertainty in the distortion variation, restoringdistorted images caused by liquify filter is a challenging task. To edit imagesin an efficient way, distorted images are expected to be restoredautomatically. This paper aims at the distorted image restoration, which ischaracterized by seeking the appropriate warping and completion of a distortedimage. Existing methods focus on the hardware assistance or the geometricprinciple to solve the specific regular deformation caused by naturalphenomena, but they cannot handle the irregularity and uncertainty ofartificial distortion in this task. To address this issue, we propose a novelgenerative and discriminative learning method based on deep neural networks,which can learn various reconstruction mappings and represent complex andhigh-dimensional data. This method decomposes the task into a rectificationstage and a refinement stage. The first stage generative network predicts themapping from the distorted images to the rectified ones. The second stagegenerative network then further optimizes the perceptual quality. Since thereis no available dataset or benchmark to explore this task, we create aDistorted Face Dataset (DFD) by forward distortion mapping based on CelebAdataset. Extensive experimental evaluation on the proposed benchmark and theapplication demonstrates that our method is an effective way for distortedimage restoration.

Generative and Discriminative Learning for Distorted Image Restoration