Leping Lin

Leping Lin
Guilin University of Electronic Technology · School of information and communication

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13
Publications
441
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102
Citations

Publications

Publications (13)
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Deep learning-based methods have made significant breakthroughs in real-time inference video super-resolution in recent years. However, these methods are prone to blurring, unnatural textures, and other distortion problems in super-resolution reconstructed video when dealing with complex environments and large-motion video scenes, which greatly aff...
Article
Full-text available
In scenes with large inter-frame motion variations, distant targets, and blurred targets, the lack of inter-frame alignment can greatly affect the effectiveness of subsequent video super-resolution reconstruction. How to perform inter-frame alignment in such scenes is the key to super-resolution reconstruction. In this paper, a new motion compensat...
Article
In this letter, it is proposed the hyperspectral image classification method based on the convolutional neural network, which is trained jointly by the reconstruction and discriminative loss functions. In the network, small convolutional kernels are cascaded with the pooling operator to perform feature abstraction, and a decoding channel composed o...
Article
In this paper, it is proposed the directional estimation model on the overcomplete dictionary, which bridges the compressed measurements of the image blocks and the directional structures of the dictionary. In the model, it is established the analytical method to estimate the structure type of a block as either smooth, single-oriented, or multiorie...
Article
Employing overcomplete dictionaries for applications captures the great interest, but the problem of recovering a signal from its random compressed measurements by taking advantage of the sparsity prior introduced by an overcomplete dictionary is very ill-posed, due to the compressed sampling operator and the redundancy of the dictionary. To achiev...
Article
In this paper, we propose a novel collaborative compressed sensing (CS) reconstruction method for natural images. The method is designed to enhance the accuracy and stability when recovering the sparse representations of image blocks on an overcomplete dictionary from the random measurements by introducing nonlocal self-similarity information. The...
Article
The l0 regularized problem in compressed sensing reconstruction is nonconvex with NP-hard computational complexity. Methods available for such problems fall into one of two types: greedy pursuit methods and thresholding methods, which are characterized by suboptimal fast search strategies. Nature-inspired algorithms for combinatorial optimization a...

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