Typhoon cloud image enhancement and reducing speckle with genetic algorithm in stationary wavelet domain

By employing discrete stationary wavelet transform (SWT), generalised cross-validation (GCV), genetic algorithm (GA), and non-linear gain operator, an efficient de-noising and enhancement algorithm for typhoon cloud image is proposed. Having implemented SWT to a typhoon cloud image, noise in a typhoon cloud image is reduced by modifying the stationary wavelet coefficients using GA and GCV at fine resolution levels. Asymptotical optimal de-noising threshold can be obtained, without knowing the variance of noise, by only employing the known input image data. GA and non-linear gain operator are used to modify the stationary wavelet coefficients at coarse resolution levels in order to enhance the details of a typhoon cloud image. Experimental results show that the proposed algorithm can efficiently reduce the speckle in a typhoon cloud image while well enhancing the detail. In order to accurately assess an enhanced typhoon cloud image´s quality, an overall score index is proposed based on information entropy, contrast measure and peak signal-noise-ratio (PSNR). Finally, comparisons between the proposed algorithm and other similar methods, which are proposed based on discrete wavelet transform, are carried out.