High frequency compensated face hallucination

Abstract

Face Hallucination is, one of a learning-based super-resolution technique that can reconstruct a high-resolution image using only one low-resolution image. However, there are often some detailed high-frequency components of the reconstructed image that cannot be recovered using this method. In this study, we proposed a high-frequency compensated face hallucination method for enhancing reconstruction performance. The proposed method can be divided into three steps: 1)high-resolution image reconstruction using a conventional hallucination method; 2)residual (high-frequency components) image recovery by “training” a residual image pair; 3)compensation of the reconstructed high-resolution image obtained in step 1 with the reconstructed residual image. Experimental results show that the high-resolution images obtained using our proposed approach are much better than those obtained by conventional hallucination.