Performance evaluation of noise reduction algorithm with median filter using improved thresholding method in pixelated semiconductor gamma camera system: A numerical simulation study

Bayesian Framework-Based Adaptive Hybrid Filtering for Speckle Noise Reduction in Ultrasound Images Via Lion Plus FireFly Algorithm

The existence of speckle noise in ultrasound (US) image processing distorts the image quality and also hinders the development of systematic approaches for US images. Numerous de-speckling schemes were established to date that concern speckle reduction; however, the models suffer from demerits like computational time, computational complexity, etc., that are to be rectified as soon as possible. This compulsion takes to the introduction of a new de-speckling model via an adaptive hybrid filter model that includes four filters like guided filter (GF), speckle-reducing bilateral filter (SRBF), rotation invariant bilateral nonlocal means filter (RIBNLM), and median filter (MF) respectively. Moreover, the novelty goes under the selection of optimal filter coefficients that make the process effective. Bayesian-based neural network is used to predict the appropriate filter coefficients, where the training library is constructed with the optimal coefficients. Along with this, the selection of optimal filter coefficients is done under the defined objective function using a new hybrid algorithm termed as Randomized FireFly (FF) update in Lion Algorithm (RFU-LA) that hybrids the concept of both LA and FF, respectively. Finally, the performance of the proposed de-speckling model is compared over that of other conventional models with respect to different performance measures. Accordingly, from the analysis, the mean MAPE of the proposed method are 39.13% and 49.28% higher than those of the wavelet filtering and hybrid filtering schemes for a noise variance of 0.1.

Preliminary study of improved median filter using adaptively mask size in light microscopic image

This study aimed to develop and evaluate an improved median filter (IMF) with an adaptive mask size for light microscope (LM) images. We acquired images of the mouse first molar using a LM at 100× magnification. The images obtained using our proposed IMF were compared with those from a conventional median filter. Several parameters such as the contrast-to-noise ratio, coefficient of variation, no-reference assessments and peak signal-to-noise ratio were employed to evaluate the image quality quantitatively. The results demonstrated that the proposed IMF could effectively de-noise the LM images and preserve the image details, achieving a better performance than the conventional median filter.