Linwei Yue

Image super-resolution

Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from the observed LR images. As SR has been developed for more than three decades, both multi-frame and single-frame SR have significant applications in our daily life. This paper aims to provide a review of SR from the perspective of techniques and applications, and especially the main contributions in recent years. Regularized SR methods are most commonly employed in the last decade. Technical details are discussed in this article, including reconstruction models, parameter selection methods, optimization algorithms and acceleration strategies. Moreover, an exhaustive summary of the current applications using SR techniques has been presented. Lastly, the article discusses the current obstacles for future research.

A locally adaptive L1−L2 norm for multi-frame super-resolution of images with mixed noise and outliers

Abstract In this paper, we present a locally adaptive regularized super-resolution model for images with mixed noise and outliers. The proposed method adaptively assigns the local norms in the data fidelity term of the regularized model. Specifically, it determines different norm values for different pixel locations, according to the impulse noise and motion outlier detection results. The L 1 norm is employed for pixels with impulse noise and motion outliers, and the L 2 norm is used for the other pixels. In order to balance the difference in the constraint strength between the L 1 norm and the L 2 norm, a strategy to adaptively estimate a weighted parameter is put forward. The experimental results confirm the superiority of the proposed method for different images with mixed noise and outliers.

Adaptive Norm Selection for Regularized Image Restoration and Super-Resolution

In the commonly employed regularization models of image restoration and super-resolution (SR), the norm determination is often challenging. This paper proposes a method to adaptively determine the optimal norms for both fidelity term and regularization term in the (SR) restoration model. Inspired by a generalized likelihood ratio test, a piecewise function is proposed to solve the norm of the fidelity term. This function can find the stable norm value in a certain number of iterations, regardless of whether the noise type is Gaussian, impulse, or mixed. For the regularization norm, the main advantage of the proposed method is that it is locally adaptive. Specifically, it assigns different norms for different pixel locations, according to the local activity measured by a structure tensor metric. The proposed method was tested using different types of images. The experimental results and error analyses verify the efficacy of the method.

Fusion of multi-scale DEMs using a regularized super-resolution method

The digital elevation model (DEM) is a significant digital representation of a terrain surface. Although a variety of DEM products are available, they often suffer from problems varying in spatial coverage, data resolution, and accuracy. However, the multi-source DEMs often contain supplementary information, which makes it possible to produce a higher-quality DEM through blending the multi-scale data. Inspired by super-resolution (SR) methods, we propose a regularized framework for the production of high-resolution (HR) DEM data with extended coverage. To deal with the registration error and the horizontal displacement among multi-scale measurements, robust data fidelity with weighted norm is employed to measure the conformance of the reconstructed HR data to the observed data. Furthermore, a slope-based Markov random field (MRF) regularization is used as the spatial regularization. The proposed method can simultaneously handle complex terrain features, noises, and data voids. Using the proposed method, we can reconstruct a seamless DEM data with the highest resolution among the input data, and an extensive spatial coverage. The experiments confirmed the effectiveness of the proposed method under different cases.

An improved ANUDEM method combining topographic correction and DEM interpolation

Void filling and anomaly replacement in conjunction with auxiliary sources of data have been widely used to improve the quality of existing problematic high-resolution digital elevation models. However, the traditional interpolation methods used for this purpose have always failed to eliminate the discrepancies between different data-sets. In this paper, an improved ANUDEM method is presented for DEM interpolation, which incorporates the idea of topographic correction using high correlation of topological structure between contour lines (CLs) from multi-scale digital elevation models (DEM). Firstly, the terrain topological structure is extracted from the CLs of a low-resolution DEM. The topographic surface correction is then undertaken based on the extracted structure, which recovers the topographic information of the sharp depressions and eminences to fit the high-resolution representation. Finally, the breaklines of the terrain surface are distilled and integrated into the denser DEM generation. The experiments undertaken confirmed the superiority of the proposed method over the other DEM interpolation methods. It is shown that the proposed method can provide results with a higher accuracy, as well as a better visual quality.

Accuracy assessment of SRTM V4.1 and ASTER GDEM V2 in high-altitude mountainous areas: A case study in Yulong Snow Mountain, China

As a significant digital representation of terrain surface, varieties of DEM products have been available to the public. The most widely used global DEM products are SRTM and ASTER GDEM. Given the comparable horizontal resolution and vertical error, accuracy validation and comparison have been of interest since the release, however, usually on a wide range. In this paper, we presented the results of accuracy assessment for ASTER GDEM v2 and SRTM v4.1 in Yulong Mountain, Yunnan province, China. Topographic map was chosen as the benchmark. The results and discussions were centered on the relationship between error distribution in elevation and mountainous hypsography based on data causes. The results revealed their levels of reliability for applied glaciology and hydrology in the typical snow mountain area.