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

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.

Segmentación morfológica y clasificación de niveles para la retinopatía diabética e hipertensiva mediante imágenes oftálmicas y redes convolucionales

“…Within these parameters, the smoothing factor plays a pivotal role in defining the extent of filtering. It's important to note that this filtering extent directly affects the sharpness and clarity of images (58). Finally, Step 07 perform image thresholding on the denoised image using the "THRESH BINARY" and "THRESH_OTSU" technique.…”

Image Denoising using Adaptive NL Means Filtering with Method Noise Thresholding

“…Various denoising technique are in use. Improved Non Local Means (NLM) and other denoising techniques proposed in recent years (1)(2)(3)(4) .…”