Selfie retoucher: subject-oriented self-portrait enhancement - Xia, Sifeng; Yang, Shuai; Liu, Jiaying (Multimedia tools and applications, 2019)
Sharing self-portraits starts trending nowadays with the boom of social networks and the rise of smartphones. However, limited by the hardware capabilities, self-portraits taken by the front cameras of portable media devices usually face quality problems such as an incomplete field of view and poor lighting style. In our paper, we introduce a selfie retoucher which enhances a self-portrait with the help of N supporting photos that share the same scene and similar shooting time. With the extra information brought by the supporting photos, a lager field of view and a better lighting style can be achieved. To accomplish this, we propose a novel subject-oriented self-portrait enhancement method with a cascaded illumination unification and photos registration framework. Based on the correspondences extracted from the input 1+ N photos, our method estimates and updates the illumination and registration coefficients in a cascaded manner. Moreover, a subject-oriented enhancement algorithm is proposed to enhance the face of the photographer in the self-portrait. We adopt a face-specific illumination correction process over the self-portrait to further improve the visual quality of the subject. After the enhancement, we globally fuse the aligned photos by a Markov Random Field based optimization method. During the fusion, a body map is additionally derived from the subject for guidance. Experimental results demonstrate that the proposed method achieves high-quality results in this novel application scenario.