An efficient de-noising algorithm for infrared image

Abstract

Employing discrete stationary wavelet transform (DSWT) and generalized cross validation (GCV), an efficient denoising algorithm for infrared image is proposed. Asymptotical optimal threshold can be obtained, without knowing the variance of noise, only employing the known input image data. Having implemented DSWT to an infrared image, additive Gauss white noise (AGWN), 1/f noise and multiplicative noise (MN) can be suppressed efficiently in the high frequency sub-bands of each decomposition level respectively. Experimental results show that the new algorithm can reduce efficiently the AGWN and 1/f noise in the infrared image while keeps the detail information of targets well. In performance index and visual quality, the new algorithm is more excellent than the de-noising algorithm based on discrete orthogonal wavelet transform (DOWT) and the conditional median value filter (MVF).