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Jung C.,Key Laboratory of Intelligent Perception | Ju J.,Key Laboratory of Intelligent Perception | Jiao L.,Key Laboratory of Intelligent Perception | Yang Y.,Key Laboratory of Intelligent Perception
Optical Engineering | Year: 2013

The dictionary-based super-resolution (SR) method has received much attention in recent years because sparse representation is very effective for image restoration tasks. By sparse representation, original image patches are represented as a sparse linear combination of atoms in an over-complete dictionary. However, the dictionary-based SR approach has some disadvantages in that it produces annoying ringing artifacts, especially along the object boundaries and is not effective in reconstructing images that contain patterns with strong edges. We enhance the dictionary-based SR using nonlocal total variation regularization. In the training stage, we jointly train two dictionaries, Dh and D1, from the low-resolution (LR) and high-resolution (HR) training image patches by using the K-singular value decomposition (KSVD) algorithm as in conventional methods. In the synthesis stage, we obtain the sparse coefficient vector from the LR test image over the LR dictionary, and reconstruct SR patches using the coefficient vector over the HR dictionary. Then, we employ nonlocal total variation regularization to remove annoying ringing artifacts and recover the patterns with strong edges in images. Experimental results on various test images demonstrate that the proposed method is very effective in enhancing the dictionary-based SR approaches in terms of quantitative performance and visual quality. © 2013 Society of Photo-Optical Instrumentation Engineers (SPIE).


Chen W.,Key Laboratory of Intelligent Perception | Jiao L.,Institute of Intelligent Information Processing
IEEE Transactions on Neural Networks | Year: 2010

This brief addresses the problem of designing adaptive neural network tracking control for a class of strict-feedback systems with unknown time-varying disturbances of known periods which nonlinearly appear in unknown functions. Multilayer neural network (MNN) and Fourier series expansion (FSE) are combined into a novel approximator to model each uncertainty in systems. Dynamic surface control (DSC) approach and integral-type Lyapunov function (ILF) technique are combined to design the control algorithm. The ultimate uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. Two simulation examples are provided to illustrate the feasibility of control scheme proposed in this brief. © 2009 IEEE.


Zhang S.,Key Laboratory of Intelligent Perception | Wang S.,Key Laboratory of Intelligent Perception | Chen B.,Key Laboratory of Intelligent Perception | Mao S.,Key Laboratory of Intelligent Perception
IEEE Geoscience and Remote Sensing Letters | Year: 2014

In this letter, a new classification method for fully polarimetric synthetic aperture radar (PolSAR) data based on three novel parameters is presented. The three parameters are derived from the eigenspace of the coherency matrix as linear combinations of its three eigenvalues. In the proposed classification method, the maximum value out of the three parameters is determined to assign a label to each image pixel, and the PolSAR image is classified into three classes accordingly. Experimental results based on NASA/JPL AIRSAR L-band data and CSA RADARSAT-2 C-band data illustrate the validity and efficacy of the procedure. © 2013 IEEE.

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