Infrared image denoising based on stationary wavelet transform using tensor

A method based on stationary wavelet transform using tensor for infrared image denoising was proposed. The noisy infrared image was decomposed by stationary wavelet transform. The low frequency approximation image was not changed. A cube was constituted by the high frequency sub-band images at horizontal, vertical and diagonal directions of the whole scales. And a third-order tensor was formed. The signal wavelet coefficients were estimated by multi-linear algebra method. The space structural relations of wavelet coefficients were not destroyed in this way. The correlations of coefficients both inter-scale and intra-scale were considered at the same time. The wavelet coefficients which were estimated in this method were better than the linear minimum mean square-error estimation (LMMSE) method. The denoised image was obtained by inverse stationary wavelet transform using the low frequency approximation image and the estimated high frequency detail images. Experiment result shows that the denoising results were better than the traditional LMMSE estimation with stationary wavelet domains in performance evaluation and visual quality. And a new thought was provided to estimate the wavelet coefficients more accurately.