Xuemei Li

Active contour method combining local fitting energy and global fitting energy dynamically

To get better segmentation results, local information and global information should be taken into consideration together. In this paper, we propose a new energy functional which combines a local intensity fitting term and an auxiliary global intensity fitting term, and we also give the method to adjust the weight of auxiliary global fitting term dynamically by using local contrast of the image. The combination of the two terms improves the accuracy of segmentation results obviously while reduces dependence on location of initial contour. The experiment results proved the effectiveness of our method.

A Simple Algorithm of Superpixel Segmentation With Boundary Constraint

As one of the most popular image oversegmentations, superpixel has been commonly used as supporting regions for primitives to reduce computations in various computer vision tasks. In this paper, we propose a novel superpixel segmentation approach based on a distance function that is designed to balance among boundary adherence, intensity homogeneity, and compactness (COM) characteristics of the resulting superpixels. Given an expected number of superpixels, our method begins with initializing the superpixel seed positions to obtain the initial labels of pixels. Then, we optimize the superpixels iteratively based on the defined distance measurement. We update the positions and intensities of superpixel seeds based on the three-sigma rule. The experimental results demonstrate that our algorithm is more effective and accurate than previous superpixel methods and achieves a comparable tradeoff between superpixel COM and adherence to object boundaries.

Mesh resizing based on hierarchical saliency detection

Mesh saliency is a perception-inspired metric for regional importance which is helpful to many aspects of mesh processing. However, existing mesh saliency cannot be used in mesh resizing directly because of the neglect of resizing direction. In this paper, we propose a region descriptor based on its vulnerability to a resizing direction, and use this descriptor to compute the regions saliency based on its contrast to neighboring regions. In order to avoid being misled by repeated small-scale features on the mesh, we put forward a hierarchical method for saliency computing. We build a hierarchical coarse-to-fine segmentations of the input mesh, and evaluate the saliency value on different levels of segmentations. Finally these saliency values are integrated into one saliency map after applying non-linear suppression. Equipped with the saliency map, a framework for non-homogeneous mesh resizing is presented. We regard every edge as a spring, and scale the mesh by stretching the edge. Based on the salience value, we build a global energy function on the mesh. Experiments show that our resizing method based on hierarchical saliency analysis can produce visually appealing results.

Cubic surface fitting to image by combination

We present a new method for constructing a fitting surface to image data. The new method is based on a supposition that the given image data are sampled from an original scene that can be represented by a surface defined by piecewise quadratic polynomials. The surface representing the original scene is known as the original surface in this paper. Unlike existing methods, which generally construct the fitting surface to the original surface using image data as interpolation data, the new method constructs the fitting surface using the image data as constraints to reverse the sampling process, which improves the approximation precision of the fitting surface. Associated with each data point and its near region, the new method constructs a quadratic polynomial patch locally using the sampling formula as constraint. The quadratic patch approximates the original surface with a quadratic polynomial precision. The fitting surface which approximates the original surface is formed by the combination of all the quadratic polynomial patches. The experiments demonstrate that compared with Bi-cubic and Separable PCC methods, the new method produced resized images with high precision and good quality.

Non-local feature back-projection for image super-resolution

Image super-resolution (SR) for a single low-resolution image is an important and challenging task in image processing. In this study, the authors propose a novel non-local feature back-projection method for image SR, which can effectively reduce jaggy and ringing artefacts common, in general, iterative back-projection (IBP) method. In their method, the objective high-resolution (HR) image is obtained by projecting reconstructed errors back to HR image iteratively. To optimise the initial HR image and constrain anisotropic errors propagation during IBP process, an efficient non-local feature interpolation algorithm is designed. Specially, edge information is used as constraints to make the interpolation surface preserve better shape. Furthermore, as post-processing, non-local similarities are utilised to remove noise and irregularities induced by errors propagation. Experimental results show that their method achieves better performance than state-of-the-art methods in terms of both quantitative metrics and visual qualities.

Salt and pepper noise removal in surveillance video based on low-rank matrix recovery

This paper proposes a new algorithm based on low-rank matrix recovery to remove salt & pepper noise from surveillance video. Unlike single image denoising techniques, noise removal from video sequences aims to utilize both temporal and spatial information. By grouping neighboring frames based on similarities of the whole images in the temporal domain, we formulate the problem of removing salt & pepper noise from a video tracking sequence as a low-rank matrix recovery problem. The resulting nuclear norm and L1-norm related minimization problems can be efficiently solved by many recently developed methods. To determine the low-rank matrix, we use an averaging method based on other similar images. Our method can not only remove noise but also preserve edges and details. The performance of our proposed approach compares favorably to that of existing algorithms and gives better PSNR and SSIM results.

A new method for image resizing

A new method for image processing, especially for image resizing, is presented. The new method produces pictures with higher precision, which is useful in 2D CT image resizing. Suppose that the given data points of an image are sampled from an original surface. The method first computes the fitting points of the original surface, then the fitting points are used to construct the interpolation surface to fit the original surface. Resizing the images by the interpolation surface makes them have higher precision and good quality. The experiments for test the efficiency of the new method shows that the resized images produced by the new method have higher precision and hence offer more detail information real application.

Multi-example feature-constrained back-projection method for image super-resolution

Example-based super-resolution algorithms, which predict unknown high-resolution image information using a relationship model learnt from known high- and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive kNN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image. Experimental results demonstrate that our method improves the visual quality of the high-resolution image.

Surface Interpolation to Image with Edge Preserving

Based on the assumption that low-resolution image is sampled from an original scene approximated by piecewise polynomial surface, this paper proposes two different constraints to make the fitting surface of the original scene preserve the images edge characteristics. We first construct a cubic parametric curve to approximate the edge in each local area and obtain an auxiliary pixel set by re-sampling the curve, then introduce a weight function to accord pixels different degrees of effects on surface reconstruction. By re-sampling the constructed surface, the enlarged image can be easily obtained. Extensive experimental results on various types of low-resolution images demonstrate that our method produces generally better results both in terms of quantitative evaluation and subjective visual quality.

Fitting to image by piecewise bi-cubic surface

The problem of constructing surface to fit image data is discussed. The data points of an image can be regarded being sampled from an original surface which can be approximated by piecewise quadratic polynomials. On each local region, a quadratic polynomial surface is constructed with the image data as constraint. The combination of all the quadratic polynomial surfaces forms the fitting surface which approximates the original surface with a quadratic polynomial precision. The experiments for comparing the new method with the existing ones are included.