# Performance Comparison of Selected Filters in Fast Denoising of Oil Palm Hyperspectral Data
## 1 Introduction
It was also tested on actual noisy hyperspectral data to recover junk bands that had been disregarded. [PERSON] and [PERSON] (2011) used a PCA-wavelet scheme to denoise hyperspectral data that had been contaminated by low-level noise. The method was designed to remove the subtle noise while preserving important details in the data. Important information in the hyperspectral data cubes was decorrelated from the noise using PCA, and the low-energy PCA output channels of the PCA were eliminated together with the noise. Their method was tested on two AVIRIS data cubes, and they claimed that their approach produced better results than other denoising methods [20]. [PERSON] et al. (2014) utilized PCA and block-matching 4D (BM 4D) to filter low-level noise from hyperspectral data. They claimed that it produced better results than their earlier studies using a PCA-wavelet scheme [21]. [PERSON] et al. (2013) employed low-rank matrix recovery (LRMR) for an efficient hyperspectral image (HSI) restoration method based on low-rank matrix restoration [22]. [PERSON] et al. (2020) claimed that nonlocal self-similarity is critical for denoising, and presented a one-way low-rank tensor recovery method to capture the structural correlations inherent in HSIs [23]. In order to combine the spatial nonlocal similarity with the low-rank characteristics of the global spectrum, nonlocal meets global (NG-Meet) presents a unified spatial-spectral paradigm for HSI denoising. [PERSON] et al. (2019) designed a nonlocal low-rank regularized tensor decomposition to fully utilize the global correlation across the spectrum and nonlocal self-similarity properties for HSI denoising [24]. [PERSON] et al. (2019) proposed a denoising approach using deep learning and a CNN to filter hyperspectral images. The approach exploited the correlation between adjacent bands in the hypercube, and they claimed that it performed better on synthetic and real data than other available methods [25]. [PERSON] et al. (2018) introduced HSI-DeNet, which employed deep convolutional neural net studies for HSI denoising. The network learned a series of multichannel 2D filters for the spatial and spectral structures of HSIs. The HSI denoising convolutional neural network (HSID-CNN) uses spatial and spectral information to recover clean images through two parallel feature extraction branches [3]. [PERSON] et al. (2022) utilized a method called SQAD that adopted a quasi-attention recurrent NN to filter noise from hyperspectral data [26]. A spatial-spectral interactive restoration (SSIR) framework was proposed by [PERSON] et al. (2023) by exploiting the complementarity of model-based and data-driven methods. This method was implemented in removing noise in HSIs. A CNN and TF were used as a module for denoising that produced content-based interactions to better capture global heterogeneity differences in HSIs and achieved local-global dependence modeling. The unsupervised unmixing module improved the generalization ability of SSIR and enabled stable denoising performance [27]. Two-dimension non-subsampled shearlet transform (NSST) and fully constrained least squares unmixing (FCLSU) were implemented by [PERSON] et al. (2016) to denoised low-noise (LN) and high-noise (HN) image bands separately. Firstly, the spectral correlation was exploited to separate the LN bands from the HN bands. Next, NSST was applied to each spectral band of the hyperspectral images, and finally a threshold technique on shearlet coefficients to denoised LN bands and applying FCLSU were used to denoise the LN bands [28]. [PERSON] et al. (2021) implemented HSI denoising and destripping with extended HSI observation model by exploiting a double low-rank (DLR) matrix decomposition method [29]. Two low-rank constraints were formulated into one unified framework by concurrently traversing the low-rank characteristic of the lexicographically ordered noise-free HSI and the low-rank structure of the stripe noise on each band of the HIS [29].