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Existing face hallucination methods based on convolutional neural networks(CNN) have achieved impressive performance on low-resolution (LR) faces in anormal illumination condition. However, their performance degrades dramaticallywhen LR faces are captured in low or non-uniform illumination conditions. Thispaper proposes a Copy and Paste Generative Adversarial Network (CPGAN) torecover authentic high-resolution (HR) face images while compensating for lowand non-uniform illumination. To this end, we develop two key components in ourCPGAN: internal and external Copy and Paste nets (CPnets). Specifically, ourinternal CPnet exploits facial information residing in the input image toenhance facial details; while our external CPnet leverages an external HR facefor illumination compensation. A new illumination compensation loss is thusdeveloped to capture illumination from the external guided face imageeffectively. Furthermore, our method offsets illumination and upsamples facialdetails alternately in a coarse-to-fine fashion, thus alleviating thecorrespondence ambiguity between LR inputs and external HR inputs. Extensiveexperiments demonstrate that our method manifests authentic HR face images in auniform illumination condition and outperforms state-of-the-art methodsqualitatively and quantitatively.

Copy and Paste GAN: Face Hallucination from Shaded Thumbnails