Face hallucination under an image decomposition perspective
MetadataShow full item record
Copyright © 2010 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
In this paper we propose to convert the task of face hallucination into an image decomposition problem, and thenuse the morphological component analysis (MCA) for hallucinating a single face image, based on a novel three-stepframework. Firstly, a low-resolution input image is up-sampled by interpolation. Then, the MCA is employed to decompose the interpolated image into a high-resolution image and an unsharp masking, as MCA can properly decompose a signal into special parts according to typical dictionaries. Finally, a residue compensation, which is based on the neighbour reconstruction of patches, is performed to enhance the facial details. The proposed method can effectively exploit the facial properties for face hallucination under the image decomposition perspective. Experimental results demonstrate the effectiveness of our method, in terms of the visual quality of the hallucinated face images.
Showing items related by title, author, creator and subject.
Xu, Xiang; Liu, Wan-Quan; Li, Ling (2013)Face hallucination has been a popular topic in image processing in recent years. Currently the commonly used performance criteria for face hallucination are peak signal noise ratio (PSNR) and the root mean square error ...
Xu, Xiang; Liu, Wan-Quan; Venkatesh, Svetha (2012)In this paper, we propose an innovative face hallucination approach based on principle component analysis (PCA) and residue technique. First, the relationship of projection coefficients between high-resolution and ...
Xu, Xiang; Liu, Wan-Quan; Li, Ling (2013)In this paper, we aim to enhance the resolution of a single face image. We introduce a method which utilizes the specific features of Curvelet to select training samples and estimate local face features. Based on different ...