scholarly journals FUSION OF MULTI-SCALE DEMS FROM DESCENT AND NAVCM IMAGES OF CHANG’E-3 USING COMPRESSED SENSING METHOD

2017 ◽  
Vol XLII-3/W1 ◽  
pp. 119-124
Author(s):  
M. Peng ◽  
W. Wan ◽  
Z. Liu ◽  
K. Di
Keyword(s):  
Sparse Representation ◽  
Input Data ◽  
Local Density ◽  
Matching Pursuit ◽  
Supplementary Information ◽  
Fusion Method ◽  
Multi Scale ◽  
Density Peaks ◽  
Spatial Coverage ◽  
Density Peaks Clustering

The multi-source DEMs generated using the images acquired in the descent and landing phase and after landing contain supplementary information, and this makes it possible and beneficial to produce a higher-quality DEM through fusing the multi-scale DEMs. The proposed fusion method consists of three steps. First, source DEMs are split into small DEM patches, then the DEM patches are classified into a few groups by local density peaks clustering. Next, the grouped DEM patches are used for sub-dictionary learning by stochastic coordinate coding. The trained sub-dictionaries are combined into a dictionary for sparse representation. Finally, the simultaneous orthogonal matching pursuit (SOMP) algorithm is used to achieve sparse representation. We use the real DEMs generated from Chang’e-3 descent images and navigation camera (Navcam) stereo images to validate the proposed method. Through the experiments, we have reconstructed a seamless DEM with the highest resolution and the largest spatial coverage among the input data. The experimental results demonstrated the feasibility of the proposed method.

Image Fusion Method Based on Multi-scale Directional Fast Guided Filter and Convolutional Sparse Representation

2019 ◽  
Vol 12 (3) ◽  
pp. 263-269
Author(s):  
Liu Xian-Hong ◽  
Chen Zhi-Bin
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Filter Bank ◽  
Guided Filter ◽  
Fusion Method ◽  
Edge Preserving ◽  
Directional Information ◽  
Multi Scale ◽  
Directional Filter ◽  
Image Fusion Method

Background: A multi-scale multidirectional image fusion method is proposed, which introduces the Nonsubsampled Directional Filter Bank (NSDFB) into the multi-scale edge-preserving decomposition based on the fast guided filter. Methods: The proposed method has the advantages of preserving edges and extracting directional information simultaneously. In order to get better-fused sub-bands coefficients, a Convolutional Sparse Representation (CSR) based approximation sub-bands fusion rule is introduced and a Pulse Coupled Neural Network (PCNN) based detail sub-bands fusion strategy with New Sum of Modified Laplacian (NSML) to be the external input is also presented simultaneously. Results: Experimental results have demonstrated the superiority of the proposed method over conventional methods in terms of visual effects and objective evaluations. Conclusion: In this paper, combining fast guided filter and nonsubsampled directional filter bank, a multi-scale directional edge-preserving filter image fusion method is proposed. The proposed method has the features of edge-preserving and extracting directional information.

scholarly journals Variable Length K-SVD: A New Dictionary Learning Approach and Multi-Stage OMP Method for Sparse Representation

2021 ◽  
Author(s):  
Mahdi Marsousi
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Input Data ◽  
Matching Pursuit ◽  
Computational Cost ◽  
Research Field ◽  
Learning Approach ◽  
Data Set ◽  
Multi Stage ◽  
Blocking Artifact

The Sparse representation research field and applications have been rapidly growing during the past 15 years. The use of overcomplete dictionaries in sparse representation has gathered extensive attraction. Sparse representation was followed by the concept of adapting dictionaries to the input data (dictionary learning). The K-SVD is a well-known dictionary learning approach and is widely used in different applications. In this thesis, a novel enhancement to the K-SVD algorithm is proposed which creates a learnt dictionary with a specific number of atoms adapted for the input data set. To increase the efficiency of the orthogonal matching pursuit (OMP) method, a new sparse representation method is proposed which applies a multi-stage strategy to reduce computational cost. A new phase included DCT (PI-DCT) dictionary is also proposed which significantly reduces the blocking artifact problem of using the conventional DCT. The accuracy and efficiency of the proposed methods are then compared with recent approaches that demonstrate the promising performance of the methods proposed in this thesis.

scholarly journals CLoNe: Automated clustering based on local density neighborhoods for application to biomolecular structural ensembles

2020 ◽  
Author(s):  
Träger Sylvain ◽  
Tamò Giorgio ◽  
Aydin Deniz ◽  
Fonti Giulia ◽  
Audagnotto Martina ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
State Of The Art ◽  
Local Density ◽  
Binding Pocket ◽  
Fine Tuning ◽  
Supplementary Information ◽  
Original Algorithm ◽  
Density Peaks ◽  
Cluster Shape ◽  
Structural Ensembles

Abstract Motivation Proteins are intrinsically dynamic entities. Flexibility sampling methods, such as molecular dynamics or those arising from integrative modeling strategies are now commonplace and enable the study of molecular conformational landscapes in many contexts. Resulting structural ensembles increase in size as technological and algorithmic advancements take place, making their analysis increasingly demanding. In this regard, cluster analysis remains a go-to approach for their classification. However, many state-of-the-art algorithms are restricted to specific cluster properties. Combined with tedious parameter fine-tuning, cluster analysis of protein structural ensembles suffers from the lack of a generally applicable and easy to use clustering scheme. Results We present CLoNe, an original Python-based clustering scheme that builds on the Density Peaks algorithm of Rodriguez and Laio. CLoNe relies on a probabilistic analysis of local density distributions derived from nearest neighbors to find relevant clusters regardless of cluster shape, size, distribution and amount. We show its capabilities on many toy datasets with properties otherwise dividing state-of-the-art approaches and improves on the original algorithm in key aspects. Applied to structural ensembles, CLoNe was able to extract meaningful conformations from membrane binding events and ligand-binding pocket opening as well as identify dominant dimerization motifs or inter-domain organization. CLoNe additionally saves clusters as individual trajectories for further analysis and provides scripts for automated use with molecular visualization software. Availability www.epfl.ch/labs/lbm/resources, github.com/LBM-EPFL/CLoNe Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Density Peaks Clustering Based on Weighted Local Density Sequence and Nearest Neighbor Assignment

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 34301-34317 ◽  
Author(s):  
Donghua Yu ◽  
Guojun Liu ◽  
Maozu Guo ◽  
Xiaoyan Liu ◽  
Shuang Yao
Keyword(s):  
Nearest Neighbor ◽  
Local Density ◽  
Density Peaks ◽  
Density Peaks Clustering

scholarly journals Density Peaks Clustering based on Nature Nearest Neighbor and Multi-cluster Mergers

2021 ◽  
Author(s):  
Hui Ma ◽  
Ruiqin Wang ◽  
Shuai Yang
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Local Density ◽  
Density Peaks ◽  
Density Peaks Clustering ◽  
Assignment Strategy ◽  
Cutoff Distance ◽  
Clustering Effect ◽  
Cluster Mergers ◽  
Selection Of

Abstract Clustering by fast search and find of Density Peaks (DPC) has the advantages of being simple, efficient, and capable of detecting arbitrary shapes, etc. However, there are still some shortcomings: 1) the cutoff distance is specified in advance, and the selection of local density formula will affect the final clustering effect; 2) after the cluster centers are found, the assignment strategy of the remaining points may produce “Domino effect”, that is, once a point is misallocated, more points may be misallocated subsequently. To overcome these shortcomings, we propose a density peaks clustering algorithm based on natural nearest neighbor and multi-cluster mergers. In this algorithm, a weighted local density calculation method is designed by the natural nearest neighbor, which avoids the selection of cutoff distance and the selection of the local density formula. This algorithm uses a new two-stage assignment strategy to assign the remaining points to the most suitable clusters, thus reducing assignment errors. The experiment was carried out on some artificial and real-world datasets. The experimental results show that the clustering effect of this algorithm is better than those other related algorithms.

scholarly journals Improved Density Peaks Clustering Based on Natural Neighbor Expanded Group

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Lin Ding ◽  
Weihong Xu ◽  
Yuantao Chen
Keyword(s):  
State Of The Art ◽  
Local Density ◽  
Clustering Technique ◽  
Expanded Group ◽  
Density Peaks ◽  
Natural Neighbor ◽  
Density Peaks Clustering ◽  
Cutoff Distance ◽  
Public Datasets ◽  
Distance Parameter

Density peaks clustering (DPC) is an advanced clustering technique due to its multiple advantages of efficiently determining cluster centers, fewer arguments, no iterations, no border noise, etc. However, it does suffer from the following defects: (1) difficult to determine a suitable value of its crucial cutoff distance parameter, (2) the local density metric is too simple to find out the proper center(s) of the sparse cluster(s), and (3) it is not robust that parts of prominent density peaks are remotely assigned. This paper proposes improved density peaks clustering based on natural neighbor expanded group (DPC-NNEG). The cores of the proposed algorithm contain two parts: (1) define natural neighbor expanded (NNE) and natural neighbor expanded group (NNEG) and (2) divide all NNEGs into a goal number of sets as the final clustering result, according to the closeness degree of NNEGs. At the same time, the paper provides the measurement of the closeness degree. We compared the state of the art with our proposal in public datasets, including several complex and real datasets. Experiments show the effectiveness and robustness of the proposed algorithm.

scholarly journals Variable Length K-SVD: A New Dictionary Learning Approach and Multi-Stage OMP Method for Sparse Representation

2021 ◽  
Author(s):  
Mahdi Marsousi
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Input Data ◽  
Matching Pursuit ◽  
Computational Cost ◽  
Research Field ◽  
Learning Approach ◽  
Data Set ◽  
Multi Stage ◽  
Blocking Artifact

The Sparse representation research field and applications have been rapidly growing during the past 15 years. The use of overcomplete dictionaries in sparse representation has gathered extensive attraction. Sparse representation was followed by the concept of adapting dictionaries to the input data (dictionary learning). The K-SVD is a well-known dictionary learning approach and is widely used in different applications. In this thesis, a novel enhancement to the K-SVD algorithm is proposed which creates a learnt dictionary with a specific number of atoms adapted for the input data set. To increase the efficiency of the orthogonal matching pursuit (OMP) method, a new sparse representation method is proposed which applies a multi-stage strategy to reduce computational cost. A new phase included DCT (PI-DCT) dictionary is also proposed which significantly reduces the blocking artifact problem of using the conventional DCT. The accuracy and efficiency of the proposed methods are then compared with recent approaches that demonstrate the promising performance of the methods proposed in this thesis.

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