<![CDATA[This research conducts a comparative evaluation of four image sharpening methods: Unsharp Masking, Laplacian of Gaussian, High-Boost Filtering, and Adaptive High-Boost Filtering. These methods are tested on low-contrast, blurred, normal, and high-contrast images. The assessment uses No Reference Image Quality Assessment metrics, specifically BRISQUE and NIQE, along with intensity histogram analysis and visual inspection. Results show that High-Boost Filtering improves global contrast, reducing BRISQUE scores to 26.28 for low-contrast images and 27.56 for high-contrast images, although it can cause halo artifacts. Unsharp Masking performs best on blurred images, lowering BRISQUE to 26.65, but it is more sensitive to noise. The Laplacian of Gaussian yields relatively low NIQE scores, such as 3.04 in low-contrast and 3.10 in high-contrast images; however, its output often appears coarse in texture. Adaptive High-Boost Filtering performs best on normal images, achieving a BRISQUE score of 11.89, but shows limited improvement in other cases. Notably, alignment between NIQE scores and perceptual evaluation is only observed in high-contrast images. These results confirm that no single technique is universally optimal, emphasizing the importance of selecting sharpening methods based on specific image degradation characteristics. Additionally, this observation highlights that BRISQUE more reliably reflects perceived image quality, whereas NIQE occasionally diverges from subjective judgments.]]>
