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Transform Learning Based Sparse Coding for LiDAR Data Denoising
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DOI：10.1109/LSP.2019.2895974.png)
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摘要
En 中文
In recent years, sparse coding (SC) has been exploited for light detection and ranging (LiDAR) data restoration, and promising results have been reported. However, such methods are usually time consuming, because much computational resource has been devoted to solving batches of l(0) or l(1)-norm optimization problems iteratively. More recently, fast SC method has been proposed to achieve nearly real-time performance at the expense of applicability. In this letter, we propose a transform learning based SC method for LiDAR data denoising. Moreover, we present a detailed evaluation for our series of SC methods with different models, and together with concluding remarks. Such remarks can be applied as a guide to apply appropriate SC model for specific application.
Keyword:
LiDAR data denoising
sparse coding
dictionary learning
transform learning
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期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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