An Improved Nimble Filter for Medical Image Sharpening

Medical image sharpening is crucial for enhancing diagnostic accuracy and visual clarity. This paper proposes an Improved Nimble Filter, an advancement over the standard Nimble Filter, designed to improve the sharpness and detail in medical images. The proposed filter incorporates an adaptive window size, a lower tuning weight that adapts to local image characteristics, and a local Otsu thresholding step to refine the sharpening process. Additionally, the filter applies a Gaussian blur to the local region of interest to reduce noise and preserve edges. These enhancements enable effective edge preservation and noise minimisation, addressing the limitations of existing sharpening techniques. The effectiveness of the Improved Nimble Filter is demonstrated through comparative analysis with other well-known methods, including the Laplacian operator, unsharp mask, shock filter, and generalised unsharp masking, using Brain MRI images from the ABIDE dataset. Experimental results show that the proposed filter significantly outperforms these techniques in terms of visual quality, edge preservation, and processing times, making it a valuable tool for medical image processing applications.