Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Non-negative sparse coding shrinkage for image denoising using normal inverse Gaussian density model
Image and Vision Computing
Denoising Natural Images Using Sparse Coding Algorithm Based on the Kurtosis Measurement
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Active MMW focal plane imaging system
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Learning PDEs for image restoration via optimal control
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
MAP image restoration and segmentation by constrained optimization
IEEE Transactions on Image Processing
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
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For the problem that a millimeter wave (MMW) image contains noise and behaves low resolution, a novel MMW image reconstruction method, combined the non-negative sparse coding shrinkage (NNSCS) technique and the partial differential equations (PDEs) algorithm (denoted by NNSCS+ PDEs), is proposed in this paper. The method of PDEs is an efficient image reconstruction technique and is easy to implement. However, MMW image is highly contaminated by much unknown noise, and the reconstruction result is not satisfied only using PDEs to process images. While the NNSCS only relies on the high-order statistical property of an image and is a self-adaptive image denoising method. Thus, combined the advantage of NNSCS and PDEs, the MMW image can be well restored. In test, a natural image is used to testify the validity of the NNSC+PDEs method, and the signal noise ratio (SNR) is used as the measure criterion of restored images. Compared with NNSCS and PDEs respectively, simulation results show that our method is indeed efficient in the task of reconstructing WWM images.