Adaptive Image Sharpening Method Using Edge Sharpness

Akira INOUE  Johji TAJIMA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E76-D   No.10   pp.1174-1180
Publication Date: 1993/10/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Machine Vision Applications)
Category: 
Keyword: 
image processing,  image sharpening,  image quality,  sharpness evaluation,  

Full Text: PDF(796.1KB)>>
Buy this Article




Summary: 
This paper proposes a new method for automatic improvement in image quality through adjusting the image sharpness. This method does not need prior knowledge about image blur. To improve image quality, the sharpness must be adjusted to an optimal value. This paper shows a new method to evaluate sharpness without MTF. It is considered that the human visual system judges image sharpness mainly based upon edge area features. Therefore, attention is paid to the high spatial frequency components in the edge area. The value is defined by the average intensity of the high spatial fequency components in the edge area. This is called the image edge sharpness" value. Using several images, edge sharpness values are compared with experimental results for subjective sharpness. According to the experiments, the calculated edge sharpness values show a good linear relation with subjective sharpness. Subjective image sharpness does not have a monotonic relation with subjective image quality. If the edge sharpness value is in a particular range, the image quality is judged to be good. According to the subjective experiments, an optimal edge sharpness value for image quality was obtained. This paper also shows an algorithm to alter an image into one which has another edge sharpness value. By altering the image, which achieves optimal edge sharpness using this algorithm, image sharpness can be optimally adjusted automatically. This new image improving method was applied to several images obtained by scanning photographs. The experimental results were quite good.