Dual tree complex wavelet transform based denoising of optical microscopy images

Abstract

Photon shot noise is the main noise source of optical microscopy images and can be modeled by a Poisson process. Several discrete wavelet transform based methods have been proposed in the literature for denoising images corrupted by Poisson noise. However, the discrete wavelet transform (DWT) has disadvantages such as shift variance, aliasing, and lack of directional selectivity. To overcome these problems, a dual tree complex wavelet transform is used in our proposed denoising algorithm. Our denoising algorithm is based on the assumption that for the Poisson noise case threshold values for wavelet coefficients can be estimated from the approximation coefficients. Our proposed method was compared with one of the state of the art denoising algorithms. Better results were obtained by using the proposed algorithm in terms of image quality metrics. Furthermore, the contrast enhancement effect of the proposed method on collagen fıber images is examined. Our method allows fast and efficient enhancement of images obtained under low light intensity conditions.

Keywords: (100.0100) Image processing; (100.3020) Image reconstruction-restoration; (100.7410) Wavelets.

Fig. 1

Decomposition with 2D DT-CWT.

Fig. 2

Test images.

Fig. 3

Aliasing effect.

Fig. 4

(a) Collagen fiber image. (Left) Recorded image. (Right) Enhanced image using proposed method. (b) Image feature selection on magnified images.