Poisson Noise Reduction with Higher-Order Natural Image Prior Model

An inherent characteristic of the many imaging modalities such as fluorescence microscopy and other microscopic modalities is the presence of intrinsic Poisson noise that may lead to degradation of the captured image during its formation. A nonlinear complex diffusion-based filter adapted to Poisson noise is proposed in this paper to restore and enhance the degraded microscopic images captured by imaging devices having photon limited light detectors. The proposed filter is based on a maximum a posterior approach to the image reconstruction problem. The formulation of the filtering problem as maximisation of a posterior is useful because it allows one to incorporate the Poisson likelihood term as a data attachment which can be added to an image prior model. Here, the Gibb's image prior model-based on energy functional defined in terms of gradient norm of the image is used. The performance of the proposed scheme has been compared with other standard techniques available in literature such as Wiener filter, regularised filter, Lucy-Richardson filter and another proposed nonlinear anisotropic diffusion-based filter in terms of mean square error, peak signal-to-noise ratio, correlation parameter and mean structure similarity index map.The results shows that the proposed complex diffusion-based filter adapted to Poisson noise performs better in comparison to other filters and is better choice for reduction of intrinsic Poisson noise from the digital microscopic images and it is also well capable of preserving edges and radiometric information such as luminance and contrast of the restored image.Defence Science Journal, 2011, 61(5), pp.452-461, DOI:http://dx.doi.org/10.14429/dsj.61.1181

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Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18062903 ◽

2021 ◽

Vol 18 (6) ◽

pp. 2903

Author(s):

Seong-Hyeon Kang ◽

Ji-Youn Kim

Keyword(s):

Light Microscopy ◽

Noise Reduction ◽

Natural Image ◽

Search Window ◽

Local Means ◽

Image Characteristics ◽

Non Local ◽

Spatial Quality ◽

Evaluation Parameters ◽

Color Channels

The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.

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