高光谱图像修复算法的自适应稀疏编码实现

Hyperspectral image inpainting based on adaptive sparse coding

针对高光谱图像(hyperspectral images,HSI)中缺损像元及条带影响图像后续处理及应用的问题,应用稀疏表示理论,将HSI修复问题建模为不完整观测下的信号稀疏重建问题,提出自适应稀疏编码实现的HSI修复算法。首先,对加性噪声假设下的HSI观测模型进行研究。然后,通过引入基于随机近似的在线学习优化方法,提出新的从高光谱数据中直接构造字典的算法,从而获取光谱字典。之后,应用变量分解和增广拉格朗日稀疏回归方法对图像进行稀疏编码求解。最后通过稀疏重构求得修复后的HSI。实验结果表明,相对于现有算法,在不同噪声条件下,所提算法均能够更有效地修复缺损的HSI,且与其他字典学习类修复算法相比计算耗时更短。

Aiming to the problem that the corrupted pixels and strips in hyperspectral image (HSI) limit the subsequent processing and applications,the sparse representation theory is applied to model HSI inpainting as an image reconstruction problem from incomplete observations,and an HSI inpainting algorithm is proposed based on adaptive sparse coding.First,an HSI incomplete observation model under the additive noise assumption is studied.Then,by introducing an online learning optimization method based on stochastic approximation,an algorithm for constructing a dictionary directly from the hyperspectral data is proposed to obtain a spectral dictionary.After that,the corrupted HSI is sparsely encoded by applying the sparse regression by variable splitting and augmented Lagrangian.Finally,the inpainted HSI is obtained by sparse reconstruction.Experiments illustrate that,compared with the state-of-the-art algorithms,the proposed algorithm can yield better inpainted results under different noise conditions,with shorter time consumption than other dictionary learning based inpainting algorithms.