Nowadays, digital images play an increasingly important role in helping to explain phenomena and to attract people's attention through various types of media rather than the use of text. However, the quality of digital images may be degraded due to noise that has occurred either during their recording or their transmission via a network. Therefore, removal of image noise, which is known as "image denoising", is one of the primary required tasks in digital image processing. Various methods in earlier studies have been developed and proposed to remove the noise found in images. For example, the use of metric filters to eliminate noise has received much attention from researchers in recent literature. However, the convergence speed when searching for the optimal filter coefficient of these proposed algorithms is quite low. Previous research in the past few years has found that biologically inspired approaches are among the more promising metaheuristic methods used to find optimal solutions. In this work, an image denoising approach based on the best-so-far (BSF) ABC algorithm combined with an adaptive filter is proposed to enhance the performance of searching for the optimal filter coefficient in the denoising process. Experimental results indicate that the denoising of images employing the proposed BSF ABC technique yields good quality and the ability to remove noise while preventing the features of the image from being lost in the denoising process. The denoised image quality obtained by the proposed method achieves a 20% increase compared with other recently developed techniques in the field of biologically inspired approaches.
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