Abstract
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.
Highlights
light microscopy (LM) images have considerably contributed to the comprehension of human mechanisms by providing functional and structural information on specimens, such as cells and tissues, in biomedical research [1,2]
An experiment was performed to analyze the effect of the fast non-local means (FNLM) algorithm with various smoothing factors on the color LM image characteristics
The authors were led to various conclusions by performing two experiments to analyze the feasibility of the color LM image processing of the FNLM algorithm
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Light microscopy (LM) is employed to capture magnified high-resolution images of objects invisible to unaided sight. LM hardware and software technologies have rapidly advanced for application in various fields. LM images have considerably contributed to the comprehension of human mechanisms by providing functional and structural information on specimens, such as cells and tissues, in biomedical research [1,2]. The technologies and application methods for LM images have been actively developed, the analysis of several colorless and transparent specimens is difficult because the structures are indistinct and the background contrast is insufficient
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
