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Speckle noise is a multiplicative type of noise commonly seen in medical and remote sensing images. It gives a granular appearance that degrades the quality of the recorded images. These speckle noise components need to be mitigated before the image is used for further processing and analysis. This paper presents a novel approach for removing granular speckle noise in gray scale images. We used an efficient multiscale image representation scheme named fast multiscale directional filter bank (FMDFB) along with simple threshold methods such as Vishushrink for image processing. It is a perfect reconstruction framework that can be used for a wide range of image processing applications because of its directionality and reduced computational complexity. The FMDFB-based speckle mitigation is appealing over other traditional multiscale approaches such as wavelets and Contourlets. Our experimental results show that the despeckling performance of the proposed method outperforms the wavelet and Contourlet-based despeckling methods.

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