Nut Image Enhancement: Effective Noise Removal with Enhanced Selective Median Filters (ESMF) Methodology

Development on optimizing ways for improving visual material has been sparked by the growing significance of image processing methods in many different industries. Image noise reduction is important, particularly in fields like food quality evaluation where precise depictions are essential. Noise removal plays a vital role in image processing as it will reduce the accuracy during segmentation and classification. In nuts image classification noise will be very high as all images are taken under natural noise such as rain, snow, fog, and mist. Filters such as Bilateral, Median, and Enhanced Selective Median Filter (ESMF) have been used to remove noise. Parameters like structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean squared error (MSE) has been evaluated. This suggests that ESMF outperforms other alternatives in maintaining structural information and overall image quality while minimizing noise. The results of this study indicate that, when compared to conventional filters, ESMF performs better, making it a viable option for noise reduction in nut images. Applying the ESMF filter produced noticeably better outcomes for each studied output parameter producing MSE of 0.000119, PSNR of 42.34, and SSIM of 0.998, respectively. The tool used for execution is python.