Superresolution Image Reconstruction Utilizing HighPrecision Skin Color for Face Images In recent years, opportunities to deal with digital images on the Internet have increased due to the information society, there is a great demand for techniques such as superresolution processing to make images more beautiful. When superresolution processing is performed on natural images such as scenery, edges are emphasized to obtain clear images. However, when superresolution processing is applied to a facial image, the wrinkle and stains of the skin as well as the emphasis of hair and eyes are emphasized, so superresolution processing on the skin part is not suitable. Therefore, in the previous study, we proposed a method to perform facial correction using nonlinear filter on skin part, and tried to solve this problem. This method is composed of superresolution processing and facial correction processing, and it was possible to realize a superresolution processing with a sharp sense for facial images. However, we also confirmed that there was a problem that the image quality deteriorated according to the skin color detection accuracy at the time of image synthesis of each processed image. Therefore, in this paper, we study the skin color detection method and try to improve the image quality.

Recovering Extremely Degraded Faces by Joint SuperResolution and Facial Composite In the past a few years, we witnessed rapid advancement in face superresolution from very low resolution(VLR) images. However, most of the previous studies focus on solving such problem without explicitly considering the impact of severe reallife image degradation (e.g. blur and noise). We can show that robustly recover details from VLR images is a task beyond the ability of current stateoftheart method. In this paper, we borrow ideas from facial composite and propose an alternative approach to tackle this problem. We endow the degraded VLR images with additional cues by integrating existing face components from multiple reference images into a novel learning pipeline with both low level and high level semantic loss function as well as a specialized adversarial based training scheme. We show that our method is able to effectively and robustly restore relevant facial details from 16x16 images with extreme degradation. We also tested our approach against reallife images and our method performs favorably against previous methods.