tailieunhanh - Báo cáo toán học: " Adaptive example-based super-resolution using kernel PCA with a novel classification approach"

Tuyển tập các báo cáo nghiên cứu khoa học ngành toán học được đăng trên tạp chí toán học quốc tế đề tài: Adaptive example-based super-resolution using kernel PCA with a novel classification approach | Ogawa and Haseyama EURASIP Journal on Advances in Signal Processing 2011 2011 138 http content 2011 1 138 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Adaptive example-based super-resolution using kernel PCA with a novel classification approach Takahiro Ogawa and Miki Haseyama Abstract An adaptive example-based super-resolution SR using kernel principal component analysis PCA with a novel classification approach is presented in this paper. In order to enable estimation of missing high-frequency components for each kind of texture in target low-resolution LR images the proposed method performs clustering of high-resolution HR patches clipped from training HR images in advance. Based on two nonlinear eigenspaces respectively generated from HR patches and their corresponding low-frequency components in each cluster an inverse map which can estimate missing high-frequency components from only the known low-frequency components is derived. Furthermore by monitoring errors caused in the above estimation process the proposed method enables adaptive selection of the optimal cluster for each target local patch and this corresponds to the novel classification approach in our method. Then by combining the above two approaches the proposed method can adaptively estimate the missing high-frequency components and successful reconstruction of the HR image is realized. Keywords Super-resolution resolution enhancement image enlargement Kernel PCA classification 1 Introduction In the field of image processing high-resolution images are needed for various fundamental applications such as surveillance high-definition TV and medical image processing 1 . However it is often difficult to capture images with sufficient high resolution HR from current image sensors. Thus methodologies for increasing resolution levels are used to bridge the gap between demands of applications and the limitations of hardware and such .