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Digital imaging is omnipresent nowadays due to the expansion of the mobile device industry and its use cases. However, the diversity of sensors and hardware limitations often lead to images not meeting desired quality standards. To counteract this, significant research has been invested in image de-noising and sharpening to enhance the quality of captured images, making image sharpening an integral step in professional image processing. Nonetheless, certain challenges persist, including the frequent occurrence of over-enhancement in parts of the image, giving rise to artefacts such as "jaggies" or jagged edges, and a "halo" effect. Recognizing the successful outcomes achieved when dilated or extended filters are employed in other image processing tasks, like edge detection, our study is set to investigate a variety of sharpening algorithms using these filter extensions. A comprehensive evaluation was conducted on synthetic and natural images using a variety of image quality metrics, like PSNR or BRISQUE. In most cases, dilated kernels outperformed extended kernels, suggesting their potential superiority in image sharpening tasks while minimizing the introduction of undesired artefacts. However, the choice between dilated and extended kernels would ultimately depend on the specific application and algorithm used.

An analysis of extended and dilated filters in sharpening algorithms