Spectral-Smoothness and Non-Local Self-Similarity Regularized Subspace Low-Rank Learning Method for Hyperspectral Mixed Denoising

Wei Liu,Chengxun He,Le Sun

doi:10.3390/rs13163196

Wei Liu, Chengxun He + Show 1 more

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https://doi.org/10.3390/rs13163196

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Abstract

During the imaging process, hyperspectral image (HSI) is inevitably affected by various noises, such as Gaussian noise, impulse noise, stripes or deadlines. As one of the pre-processing steps, the removal of mixed noise for HSI has a vital impact on subsequent applications, and it is also one of the most challenging tasks. In this paper, a novel spectral-smoothness and non-local self-similarity regularized subspace low-rank learning (termed SNSSLrL) method was proposed for the mixed noise removal of HSI. First, under the subspace decomposition framework, the original HSI is decomposed into the linear representation of two low-dimensional matrices, namely the subspace basis matrix and the coefficient matrix. To further exploit the essential characteristics of HSI, on the one hand, the basis matrix is modeled as spectral smoothing, which constrains each column vector of the basis matrix to be a locally continuous spectrum, so that the subspace formed by its column vectors has continuous properties. On the other hand, the coefficient matrix is divided into several non-local block matrices according to the pixel coordinates of the original HSI data, and block-matching and 4D filtering (BM4D) is employed to reconstruct these self-similar non-local block matrices. Finally, the formulated model with all convex items is solved efficiently by the alternating direction method of multipliers (ADMM). Extensive experiments on two simulated datasets and one real dataset verify that the proposed SNSSLrL method has greater advantages than the latest state-of-the-art methods.

Highlights

Hyperspectral image (HSI) has played an important role in many modern scenarios like urban planning, agricultural exploration, criminal investigation, military surveillance, etc. [1]
To verify the denoising performance of the proposed algorithm, six representative state-ofthe-art HSI denoising methods are used as competitors, i.e., SLRL4D [61], 3DlogTNN [58], LRTDGS [63], LRTDTV [55], LLRSSTV [64], FSSLRL [60]
The parameters of the comparison methods are first set to the recommended parameter settings in the corresponding references and those values associated to the optimal results for each dataset are adopted after fine-tuning

Summary

Introduction

Hyperspectral image (HSI) has played an important role in many modern scenarios like urban planning, agricultural exploration, criminal investigation, military surveillance, etc. [1]. The existing mixed noise generated during the process of digital imaging, e.g., Gaussian noise, salt and pepper, stripes and deadlines, seriously diminishes the accuracy of the above applications [2]. As a key preprocessing step of the above-mentioned applications, the studies on HSI mixed denoising methods have become a hot research topic and aroused widespread interest in the field. Many methods of HSI denoising have been proposed. As each band of HSI can be regarded as a two-dimensional matrix, the simplest way is to directly use the traditional and classic two-dimensional image denoising method, such as Total Variation (TV) [11], K-SVD [12], NLM [13], BM3D [14], WNNM [15]. Part of the methods are based on the domain transform theory, that is, data are transformed from the original spatial

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