Abstract
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.
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This work was supported in part by National Natural Science Foundation of China under contract No. 61772043 and in part by Beijing Natural Science Foundation under contract No. L182002 and No. 4192025.
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Xia, S., Yang, S. & Liu, J. Selfie retoucher: subject-oriented self-portrait enhancement. Multimed Tools Appl 78, 27591–27609 (2019). https://doi.org/10.1007/s11042-019-07873-x
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DOI: https://doi.org/10.1007/s11042-019-07873-x