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In this paper, we propose an adaptive regularizer learning method in the framework of MAP for low rank approximation (ARLLR). We assume that the prior distribution of the singular values is Laplacian with varying scale parameters. By using a full maximize a posterior (MAP) we learn the optimal scale parameters iteratively. We indicate that ARLLR is equivalent to low rank approximation regularized...
In this paper we introduce a novel decolorization strategy built on image fusion principles. Decolorization (color-to-grayscale), is an important transformation used in many monochrome image processing applications. We demonstrate that aside from color spatial distribution, local information plays an important role in maintaining the discriminability of the image conversion. Our strategy blends the...
When applying a filter to an image, it often makes practical sense to maintain the local brightness level from input to output image. This is achieved by normalizing the filter coefficients so that they sum to one. This concept is generally taken for granted, but is particularly important where nonlinear filters such as the bilateral or and non-local means are concerned, where the effect on local...
This paper introduces a novel class of transforms, called graph-based separable transforms (GBSTs), based on two line graphs with optimized weights. For the optimal GBST construction, we formulate a graph learning problem to design two separate line graphs using row-wise and column-wise residual block statistics, respectively. We also analyze the optimality of resulting separable transforms for both...
We address the problem of how to design a more effective co-training scheme to tackle the multi-view spectral clustering. The conventional co-training procedure treats information from all views equally and often converges to a compromised consensus view that does not fully utilize the multiview information. We instead propose to learn an augmented view and construct its corresponding affinity matrix...
Graphs have been widely used in image processing and understanding tasks. We introduce a novel graph generation model which greatly reduces the size of the traditional pixel-based graph. Based on the generated graph, we propose two feature extraction methods which utilize spectral graph information, and apply the features to image. Experiments show that our proposed oscillatory image heat content...
Most edge-aware smoothing methods are based on the Euclidean distance to measure the similarity between adjacent pixels. This paper exploits the properties of the commute time to extend the notion of “similarity” in this context. The intuition is that since the commute time reflects the effect of all possible weighted paths between nodes (pixels), it can account for the global distribution of image...
Cutting out and object and estimate its transparency mask is a key task in many applications. We take on the work on closed-form matting by Levin et al.[1], that is used at the core of many matting techniques, and propose an alternative formulation that offers more flexible controls over the matting priors. We also show that this new approach is efficient at upscaling transparency maps from coarse...
In this work, a new simple but effective fusion-based strategy for enhancing single backlit image is proposed. The fundamental idea of proposed strategy is to blend different features into a single one to improve the specific quality of image. Most of existing methods are based on the modification of histogram to enhance the contrast of low light images. However, the backlit images are different from...
Unsupervised segmentation and contour detection remains a challenging task. In graph-based unsupervised segmentation, the formulation of the affinity graph is pivotal to segmentation performance. Conventional graph-based approaches often only define pixels as graph nodes, and may overlook important regional information. In this paper, we propose a novel scheme for affinity graph construction, where...
In quest for high spectral fidelity in spatial superresolution of multispectral images, we explore physically-induced, joint spectral-spatial sparsities. The bichromatic image formation model is used to reveal that the discontinuities of a multi-spectral image tend to align spatially across different spectral bands; in other words, the 2D Laplacians of different bands are not only sparse but also...
Brain imaging data such as EEG or MEG is high-dimensional spatiotemporal measurements that commonly require dimensionality reduction before being used for further analysis or applications. This paper presents a new dimensionality reduction method based on the recent graph signal processing theory. Specifically, we focus on a task to classify the brain imaging signals recording the cortical activities...
Activity recognition and activity boundary detection are two separate long-standing challenges in the image processing literature. In activity recognition, a predefined set of activities is classified using features. Often, subjects do not perform meaningful activities in all the frames, thus requiring the identification of the beginning and the end of the set of contiguous frames containing the activity...
We propose a new method for robust learning Laplacian matrices from observed smooth graph signals in the presence of both Gaussian noise and random-valued impulse noise (i.e., outliers). Using the recently developed factor analysis model for representing smooth graph signals in [1], we formulate our learning process as a constrained optimization problem, and adopt the £i-norm for measuring the data...
Methods of seismic horizon reconstruction based on the solution of a partial derivative equation are generally robust to noise. However, they can only reconstruct horizons on rectangular domains when using fast iterative methods which is inconvenient in regions of uncertainty or irrelevant data in the seismic image. In this paper, we propose a method to reconstruct seismic horizons on any polygonal...
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