Wavelet-based medical infrared image noise reduction using local model for signal and noise

This paper presents a new wavelet-based denoising method for medical infrared images. Since the dominant noise in infrared images is signal dependent we use local models for statistical properties of (noise-free) signal and noise. In this base, the noise variance is locally modeled as a function of the image intensity using the parameters of the image acquisition protocol. In the next step, the variance of noise-free image is locally estimated and the local variances of noise-free image and noise are substituted in a wavelet-based maximum a posterior (MAP) estimator for noise removal. Our simulations illustrate that proposed technique outperforms other denoising methods including non-local methods.