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.
KEYWORDS: Image enhancement, Image filtering, Smoothing, Nonlinear filtering, Algorithm development, Image processing, High dynamic range imaging, Control systems, Digital filtering, 3D image processing
Recently many computational photography applications need to decompose an image into a piecewise smooth base layer, containing large-scale variations in intensity, and a residual detail layer capturing the smaller-scale details in the image. In these applications, the image decomposition method requires multiscale ability to avoid visual artifacts. In this paper, we propose a new model of image decomposition that has the properties of edge-preserving and multiscale ability. Inspired by techniques in computational geometry and morphological image analysis, we use the α -scale space of the input image to extract information about oscillations. We define detail as oscillations between upper and lower envelope of the input image. Building on the key observation that the spatial scale of oscillations is characterized by the α value, we develop an algorithm for decomposing images into multiple scales of superposed oscillations. Compared with traditional image decomposition methods, our method has three advantages as follows: (1) precisely controls scale parameter; (2) preserves edge while decomposing; and (3) decouples noise layer from noisy image. Finally, we compare our results with current existing edge-preserving image decomposition algorithms and demonstrate applications.
The Reeb graph provides a structure that encodes the topology of a shape, and it has been gaining in popularity in shape analysis and understanding. We introduce a spectral clustering method to compute the Reeb graph. Given a 3-D model embedded in the Euclidean space, we define the Morse function according to the connected components of the 3-D model in a spectral space. The spectral clustering formulation gives rise to a consistent Reeb graph over pose changes of the same object with meaningful subparts and yields a hierarchical computation of the Reeb graph. We prove that this method is theoretically reasonable, and experimental results show its efficiency.