Bal Ufuk
Faculty of Technology, Muğla Sıtkı Koçman University, 48000 Kötekli/Muğla, Turkey.
Biomed Opt Express. 2012 Dec 1;3(12):3231-9. doi: 10.1364/BOE.3.003231. Epub 2012 Nov 13.
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.
光子散粒噪声是光学显微镜图像的主要噪声源，可通过泊松过程进行建模。文献中已经提出了几种基于离散小波变换的方法来对受泊松噪声污染的图像进行去噪。然而，离散小波变换（DWT）存在诸如平移方差、混叠和缺乏方向选择性等缺点。为了克服这些问题，我们提出的去噪算法中使用了双树复数小波变换。我们的去噪算法基于这样一个假设，即对于泊松噪声情况，可以从小波系数的近似系数中估计小波系数的阈值。我们将提出的方法与一种最先进的去噪算法进行了比较。在图像质量指标方面，使用所提出的算法获得了更好的结果。此外，还研究了所提出的方法对胶原纤维图像的对比度增强效果。我们的方法能够快速有效地增强在低光照强度条件下获得的图像。
