Showing papers by "Hui Li published in 2018"

Real-world Noisy Image Denoising: A New Benchmark.

Abstract: Most of previous image denoising methods focus on additive white Gaussian noise (AWGN). However,the real-world noisy image denoising problem with the advancing of the computer vision techiniques. In order to promote the study on this problem while implementing the concurrent real-world image denoising datasets, we construct a new benchmark dataset which contains comprehensive real-world noisy images of different natural scenes. These images are captured by different cameras under different camera settings. We evaluate the different denoising methods on our new dataset as well as previous datasets. Extensive experimental results demonstrate that the recently proposed methods designed specifically for realistic noise removal based on sparse or low rank theories achieve better denoising performance and are more robust than other competing methods, and the newly proposed dataset is more challenging. The constructed dataset of real photographs is publicly available at \url{this https URL} for researchers to investigate new real-world image denoising methods. We will add more analysis on the noise statistics in the real photographs of our new dataset in the next version of this article.

Multi-Exposure Fusion with CNN Features

Abstract: Multi-exposure fusion (MEF) is a widely used approach to high dynamic range imaging The selection of features for fusion weight calculation is important to the performance of MEF In this paper, we investigate the effectiveness of convolutional neural network (CNN) features for MEF Considering the fact that there are no ground-truth images in MEF to train an end-to-end CNN, we adopt the pre-trained networks in other tasks to extract the feature Both the selection of network and the selection of convolution layer are studied With the extracted CNN feature map, we compute the local visibility and consistency maps to determine the weight map for MEF The proposed method works well for both static and dynamic scenes It exhibits competitive quantitative measures, and presents perceptually pleasing MEF outputs with little halo effects

A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization

Abstract: Iterative thresholding is a dominating strategy for sparse optimization problems. The main goal of iterative thresholding methods is to find a so-called $k$ -sparse solution. However, the setting of regularization parameters or the estimation of the true sparsity are nontrivial in iterative thresholding methods. To overcome this shortcoming, we propose a preference-based multiobjective evolutionary approach to solve sparse optimization problems in compressive sensing. Our basic strategy is to search the knee part of weakly Pareto front with preference on the true $k$ -sparse solution. In the noiseless case, it is easy to locate the exact position of the $k$ -sparse solution from the distribution of the solutions found by our proposed method. Therefore, our method has the ability to detect the true sparsity. Moreover, any iterative thresholding methods can be used as a local optimizer in our proposed method, and no prior estimation of sparsity is required. The proposed method can also be extended to solve sparse optimization problems with noise. Extensive experiments have been conducted to study its performance on artificial signals and magnetic resonance imaging signals. Our experimental results have shown that our proposed method is very effective for detecting sparsity and can improve the reconstruction ability of existing iterative thresholding methods.

A differential prediction model for evolutionary dynamic multiobjective optimization

Abstract: This paper introduces a differential prediction model to predict the varying Pareto-Optimal Solutions (POS) when solving dynamic multiobjective optimization problems (DMOPs). In dynamic multiobjective optimization problems, several competing objective functions and/or constraints change over time. As a consequence, the Pareto-Optimal Solutions and/or Pareto-Optimal Front may vary over time. The differential prediction model is used to forecast the shift vector in the decision space of the centroid in the population through the centroid's historical locations in three previous environments. This differential prediction model is incorporated into a multiobjective evolutionary algorithm based on decomposition to solve DMOPs. After detecting the environmental change, half of individuals in the population are forecasted their new positions in the decision space by using the differential prediction model and the others' positions are retained. The proposed model is tested on a number of typical benchmark problems with several dynamic characteristics. Experimental results show that the proposed model is competitively in comparisons with the other state-of-the-art models or approaches that were proposed for solving DMOPs.

MOEA/D with chain-based random local search for sparse optimization

Abstract: The goal in sparse approximation is to find a sparse representation of a system. This can be done by minimizing a data-fitting term and a sparsity term at the same time. This sparse term imposes penalty for sparsity. In classical iterative thresholding methods, these two terms are often combined into a single function, where a relaxed parameter is used to balance the error and the sparsity. It is acknowledged that the setting of relaxed parameter is sensitive to the performance of iterative thresholding methods. In this paper, we proposed to address this difficulty by finding a set of nondominated solutions with different sparsity levels via multiobjective evolutionary algorithms (MOEAs). A new MOEA/D is developed specifically for sparse optimization, in which a chain-based random local search (CRLS) is employed for optimizing subproblems with various sparsity levels. The performance of the proposed algorithm, denoted by MOEA/D-CRLS, is tested on a set of sixteen noise-free or noisy test problems. Our experimental results suggest that MOEA/D-CRLS is competitive regarding the solution precision on the noise-free test problems, and clearly superior on the noisy test problems against three existing representative sparse optimization methods.

Broken rock auxiliary device of microwave and combined type cantilever entry driving machine

Abstract: The utility model provides a broken rock auxiliary device of microwave and combined type cantilever entry driving machine, it includes the mounting bracket, the mounting bracket has two holes that liein same side and the setting of upper and lower interval, and it has flexible hydro -cylinder to hinge with last position hole, and it has the swing hydro -cylinder to hinge with lower position hole,the swing hydro -cylinder the opposite side with the cylinder portion of flexible hydro -cylinder is articulated, be equipped with a hinged ear on the piston rod of flexible hydro -cylinder the position department that hinged ear kept away from cylinder portion is equipped with a microwave module Compared with the prior art, the utility model provides a broken rock auxiliary device of microwavepasses through the microwave and adds hot dry rock stone fast, is showing mechanics characteristics such as reducing its loading intensity, unipolar compressive strength and tensile strength The utility model provides a combined type cantilever entry driving machine that has the broken rock auxiliary device of this microwave

Task Scheduling Methods of Marine Normal Observation Based on DE Algorithm

Abstract: Marine mission planning plays a significant role in ocean resources management. It is the process to observe ocean resources with the object to maximize the tasks required by users and maximize the utilization of ocean observation resources. Aiming to this problem, this paper proposed a generally applicable ocean normal observation task scheduling model based on the Differential Evolution algorithm. Experimental results demonstrate that the proposed model achieves significant performance by comparing to other algorithms.

Information Literacy Assessment with a Modified Hybrid Differential Evolution with Model-Based Reinitialization

Abstract: Information literacy assessment is extremely important for the evaluation of the information literacy skills of college students. Intelligent optimization technique is an effective strategy to optimize the weight parameters of the information literacy assessment index system (ILAIS). In this paper, a new version of differential evolution algorithm (DE), named hybrid differential evolution with model-based reinitialization (HDEMR), is proposed to accurately fit the weight parameters of ILAIS. The main contributions of this paper are as follows: firstly, an improved contraction criterion which is based on the population entropy in objective space and the maximum distance in decision space is employed to decide when the local search starts. Secondly, a modified model-based population reinitialization strategy is designed to enhance the global search ability of HDEMR to handle complex problems. Two types of experiments are designed to assess the performance of HDEMR. In the first type of experiments, HDEMR is tested and compared with seven well-known DE variants on CEC2005 and CEC2014 benchmark functions. In the second type of experiments, HDEMR is compared with the well-known and widely used deterministic algorithm DIRECT on GKLS test classes. The experimental results demonstrate the effectiveness of HDEMR for global numerical optimization and show better performance. Furthermore, HDEMR is applied to optimize the weight parameters of ILAIS at China University of Geosciences (CUG), and satisfactory results are obtained.