Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior

Lina Zhuang,Michael K. Ng,Xiyou Fu

doi:10.3390/rs13204098

Abstract

The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio (SNR) of the measurements. The decreased SNR reduces the reliability of measured features or information extracted from HSIs, thus calling for effective denoising techniques. This work aims to estimate clean HSIs from observations corrupted by mixed noise (containing Gaussian noise, impulse noise, and dead-lines/stripes) by exploiting two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain. We take advantage of the spectral low-rankness of HSIs by representing spectral vectors in an orthogonal subspace, which is learned from observed images by a new method. Subspace representation coefficients of HSIs are learned by solving an optimization problem plugged with an image prior extracted from a neural denoising network. The proposed method is evaluated on simulated and real HSIs. An exhaustive array of experiments and comparisons with state-of-the-art denoisers were carried out.

Highlights

We focus on the discussion of additive and signal-independent noise and attack hyperspectral mixed noise composed of these additive noises
This paper introduces a hyperspectral mixed noise removal method, HySuDeep, by exploiting two important characteristics of hyperspectral images (HSIs)
HySuDeep takes advantage of the spectral low-rankness of HSIs by representing clean spectral vectors in a low-dimensional subspace, which significantly improves the conditioning of the denoising problem

Summary

Introduction

Hyperspectral cameras measure the radiation arriving at a sensor with high spectral resolution over a sufficiently broad spectral band such that the acquired spectrum can be used to uniquely characterize and identify any given material [1]. A hyperspectral image (HSI) is a three-dimensional data cube, where the first two dimensions represent the spatial domain and the third dimension represents the spectral domain. In contrast to multispectral imaging, hyperspectral cameras capture electromagnetic information in hundreds of narrow spectral bands, instead of multiple wide spectral bands. Due to the decrease of the width of spectral bands, hyperspectral cameras receive fewer photons per band and tend to acquire images with a lower signal-to-noise ratio (SNR). The decreased SNR reduces the reliability of measured features or information extracted from HSIs [1]. Hyperspectral image denoising is a fundamental inverse problem before further applications

Methods

Results

Conclusion