GRD Journals- Global Research and Development Journal for Engineering | Volume 2 | Issue 7 | June 2017 ISSN: 2455-5703
Hyperspectral Image Denoising by using Hybrid Thresholding Spatio Spectral Total Variation Jasdeep Kaur Department of Electronics and Communication Engineering Punjabi University Patiala Er. Bhawna Utreja Department of Electronics and Communication Engineering Punjabi University Patiala
Dr. Charanjit Singh Department of Electronics and Communication Engineering Punjabi University Patiala
Abstract This paper introduces a hyperspectral denoising algorithm hinged on hybrid spatio-spectral total variation. The denoising issue have been hatched as a mixed noise diminution issue. A prevalent noise model has been pondered which reckon for not only Gaussian noise but also sparse noise. The inborn composition of hyperspectral images has been manipulated by using 2-D total variation along the spatial dimension and 1-D total variations along the spectral dimensions. The image denoising issues has been contrived as optimization hitch whose results has been acquired using the split-Bregman approach. The proposed method can minimize a remarkable amount of noise from real noisy hyperspectral images which is demonstrated by observational results. The proffer technique has been compared with prevailing avant-garde approaches. The outcomes reveal an excellence of the proposed method in the form of peak signal-to-noise ratio, structural similarity index and the visual quality. Keywords- Hyperspectral Denoising, Hybrid Spatio-Spectral Total Variation (HSSTV), Optimization, Split-Bregman
I. INTRODUCTION Image denoising is a challenging domain. It is the main issue that occurs in image acquisition process. If an image contains unwanted or some information is lost it is known as noisy image. This undesired information can be in the form of random signals which cause a change in actual intensity value of all pixels [1]. The different type of noise present in image are gaussian noise, salt and pepper noise, random-valued impulse, line strips and shot noise. Hyperspectral imaging (HIS) examines the study of interaction between matter and radiated energy. Images recorded over hundred of electromagnetic spectra varying from 400nm to 2500nm are prevalently known as hyperspectral images [2].These images are beneficial in different aspects such as space research, military and defence, process manufacturing, agriculture, forensics etc. Image denoising is preprocess in many applications [3]. Images are adulterated by noise due to various reasons such as dark current, variation in power supply and nonuniformity of detector response. Hyperspectral denoising is an assorted noise diminution issue consisting of mixture of Gaussian and sparse noise. The noise which corrupts only few pixels in the images but corrupts them heavily is called sparse noise [2]- [4]. The sparse noise involves random valued impulse noise, salt-and-pepper noise, and horizontal and vertical deadlines. In this paper, we intend to diminish mingled Gaussian and sparse noise from hyperspectral images by clearly seeing them in the planned problematic design [5] - [6]. There are various denoising methods [7]- [8] which ponder mixed noise drop from grayscale imageries. We address a representative situation by considering general noise and effort to resolve this problematic for hyperspectral images. A recent low-rank matrix recovery (LRMR)-based denoising approach [5] can diminish mixed noise from hyperspectral images. The low-rank-based model is a world-wide model which, in context of hyperspectral images, exploits spectral correlation, whereas total variation is a local model which exploits spatial correlation within a band. The proposed hybrid spatio-spectral total variation (SSTV) [9]model which is total variation model and accounts for both the spatial and the spectral correlation. The consequential optimization issue has been resolved using a split-Bregman technique [10]. We have compared our technique with prevailing method namely, LRMR [2], wavelet thresholder principle component analysis(PCAW) [11],general synthesis prior(GSP) [12] and SSTV [9].Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) [13]- [14] are used to quantify the denoising results. Experimental outcomes demonstrate that our proposed method is better than prevailing techniques. Section II represents the problem formulation, followed by Section III where we describe the method to solve the proposed formulation. Section IV demonstrates the experimental results, and Section V concludes this paper with some forthcoming directions.
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