To sharpen or not to sharpen? On the effect of sharpening filters applied to magnified images
Leonardo Ramos Emmendörfer, Vinicius Menezes R. de Olviveira, Tatiana R. Schein, Junior Costa de Jesus, Barbara R. Rodriguez · Fourteenth International Conference on Digital Image Processing (ICDIP 2022) · 2022
Super-resolution algorithms aim to produce magnified high-resolution versions from low-resolution images. Some methods, however, are prone to generate blur during the process. Simple sharpening filters are adopted to alleviate this type of artifact. However, the actual effectiveness of this approach is not clear-cut in the literature. This work evaluates the effect of three simple sharpening filters on the quality of images obtained from super-resolution methods. Two metrics were considered in the evaluation: the Peak signal-to-noise ratio (PSNR) metric, and the Learned Perceptual Image Patch Similarity (LPIPS). One of the filters could consistently improve the LPIPS metric of magnified images from diverse benchmark sets on top of seven super-resolution methods. The increments obtained for the perceptual metric seem to occur due to the sharpening effect. Improvements on PSNR values were not as consistent.