Chromatic Aberration Detection Based on Image Segmentation

Abstract

Chromatic Aberration (CA) is an active research topic in the digital era since everybody communicates through digital photos more than they ever do in the past. The digital photos or digital images are not only used to record the precious memories of people but they are also used as a mean to share those precious time with other people. The digital images thus have significant impact to our everyday life as well as the social. CA can distort the memories represented by the digital images since it is a blurring of colors especially Red and Blue colors. CA is a result from using low quality lens, which are part of digital cameras. The low quality lens disperses the light out of the incident point on the other lens and this phenomenon cause the aberration of colors. There were several attempts to detect the CA using pixel-based algorithms and filtering techniques. Some attempts spent too much effort to detect CA but got poor results. However, none of the previous attempts investigated the detection of CA using image segmentation. Therefore, this paper applies image segmentation method to detect CA and compares its performances to the existing methods of detecting CA. The unique characteristics of CA are applied to the selected image segmentation method in order to make it be able to identify the CA segments in the digital images. The preliminary experiments showed that the proposed exploitation can bring out the ability to detect CA with impressive results. The accuracy of the proposed method is up to 95.25% on the average with low false positive rate of 0.90% on the average. Moreover, the proposed method is 42.73% faster than the previous method on the average.