Supervised Discriminant Nonnegative Matrix Factorization Method

2009 ◽  
Author(s):  
Xaobing Pei ◽  
Laiyuan Xiao
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Factorization Method

Underwater Image Enhancement with the Low-Rank Nonnegative Matrix Factorization Method

2021 ◽  
pp. 2154022
Author(s):  
Xiaopeng Liu ◽  
Cong Liu ◽  
Xiaochen Liu
Keyword(s):  
Image Enhancement ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Ground Truth ◽  
Factorization Method ◽  
Low Rank ◽  
Ground Truth Data ◽  
Underwater Image ◽  
Better Than

Due to the scattering and absorption effects in the undersea environment, underwater image enhancement is a challenging problem. To obtain the ground-truth data for training is also an open problem. So, the learning process is unavailable. In this paper, we propose a Low-Rank Nonnegative Matrix Factorization (LR-NMF) method, which only uses the degraded underwater image as input to generate the more clear and realistic image. According to the underwater image formation model, the degraded underwater image could be separated into three parts, the directed component, the back and forward scattering components. The latter two parts can be considered as scattering. The directed component is constrained to have a low rank. After that, the restored underwater image is obtained. The quantitative and qualitative analyses illustrate that the proposed method performed equivalent or better than the state-of-the-art methods. Yet, it’s simple to implement without the training process.

scholarly journals Graph Sparse Nonnegative Matrix Factorization Algorithm Based on the Inertial Projection Neural Network

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangguang Dai ◽  
Chuandong Li ◽  
Biqun Xiang
Keyword(s):  
Neural Network ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Optimal Solution ◽  
Factorization Method ◽  
Real World Data ◽  
Factorization Problem ◽  
Sparse Nonnegative Matrix Factorization ◽  
And Performance

We present a novel method, called graph sparse nonnegative matrix factorization, for dimensionality reduction. The affinity graph and sparse constraint are further taken into consideration in nonnegative matrix factorization and it is shown that the proposed matrix factorization method can respect the intrinsic graph structure and provide the sparse representation. Different from some existing traditional methods, the inertial neural network was developed, which can be used to optimize our proposed matrix factorization problem. By adopting one parameter in the neural network, the global optimal solution can be searched. Finally, simulations on numerical examples and clustering in real-world data illustrate the effectiveness and performance of the proposed method.

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