Abstract
We describe a method to estimate noise power using the minimum statistics approach, which was originally proposed for audio processing. The proposed minimum statistics-based method separates a noisy image into multiple frequency bands using the wavelet packet transform. By assuming that the output of the high-pass filter contains both signal detail and noise, the proposed algorithm extracts the region of pure noise from the high frequency band using an appropriate threshold. The region of pure noise, which is free from the signal detail and the DC component, is well suited for minimum statistics conditions, in which the noise power can easily be extracted. The proposed algorithm significantly reduces the computational load by using a simple, iteration-free processing architecture, and provides an estimation accuracy rate of over 96 % for strong noise at an SNR of 0 to 30 ㏈ in the input image. Experimental results show that the proposed algorithm can accurately estimate noise power, and is particularly suitable for fast, low-cost image restoration or enhancement applications.
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