In image processing and computational photography, automatic image enhancement is one of the long-range
objectives. Recently the automatic image enhancement methods not only take account of the globe semantics, like
correct color hue and brightness imbalances, but also the local content of the image, such as human face and sky of
landscape. In this paper we describe a new scheme for automatic image enhancement that considers both global
semantics and local content of image. Our automatic image enhancement method employs the multi-scale edge-aware
image decomposition approach to detect the underexposure regions and enhance the detail of the salient content. The
experiment results demonstrate the effectiveness of our approach compared to existing automatic enhancement methods.
Normal estimation is an essential step in point cloud based geometric processing, such as high quality point based
rendering and surface reconstruction. In this paper, we present a clustering based method for normal estimation which
preserves sharp features. For a piecewise smooth point cloud, the k-nearest neighbors of one point lie on a union of
multiple subspaces. Given the PCA normals as input, we perform a subspace clustering algorithm to segment these
subspaces. Normals are estimated by the points lying in the same subspace as the center point. In contrast to the previous
method, we exploit the low-rankness of the input data, by seeking the lowest rank representation among all the
candidates that can represent one normal as linear combinations of the others. Integration of Low-Rank Representation
(LRR) makes our method robust to noise. Moreover, our method can simultaneously produce the estimated normals and
the local structures which are especially useful for denoise and segmentation applications. The experimental results show
that our approach successfully recovers sharp features and generates more reliable results compared with the state-of-theart.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.