Hyperspectral image denoising: A least square approach using wavelet filters
S. Vishnu, Sachin Rajan, Vemula Jasmine Sowmya, K. P. Soman · 2017
An image is an artifact that depicts visual perception, having a similar appearance to an object or person, thus providing a depiction of it. Images accomodate various types of noises which are due to sensor defects, lens distortion, software artifacts, blur etc. Denoising an image not only aims at removing the undesired noise but also, at retaining the features of the original image. The same goes for hyperspectral images which have numerous bands (each band contains information of the same object or location taken under different wavelengths of light) as compared to the red, green and blue bands of a color image. The need for better denoising techniques have brought about the birth of different image denoising algorithms, each with its own unique characteristics. Total Variation Denoising (TVD) is an advent for noise removal developed so as to retain sharp edges in the underlying signal. It is characterised as an optimization problem. Denoising using Legendre-Fenchel transform also widely used. Least Square based denoising technique is computationally demanding and gives better results. This paper compares the efficiency of various image denoising techniques like, total variation denoising, legendre-fenchel transform and wavelet transform denoising with the proposed method of least square denoising. This paper focuses on the hyperspectral image denoising technique based on least square approach using different wavelet filters. The proposed technique gives satisfactory denoising output with less computational time when compared with existing methods.