An objective evaluation method on image sharpness under different illumination imaging condition

An objective evaluation method on image sharpness under different illumination imaging condition

Huan He¹, Chenyang Shi^1*, Yandan Lin^2*

¹School of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, Anhui, China

²Department of Illuminating Engineering & Light Sources, School of Information Science and Technology,Fudan University, Shanghai 200433, China

*Contact email address: *** (Email)

Abstract—Blurriness is annoying yet common in digital images under different illumination imaging condition.To obtain a accurate real blurred image quality assessment (IQA), a machine learning based objective evaluation method for image sharpness under different illumination imaging conditions is proposed. In this method, the visual saliency, color difference, and gradient information are selected as the image features, and the relevant feature information of these three aspects is extracted from the image as the feature value for the real blurred image evaluation under different illumination imaging conditions. Then, a particle swarm optimization based general regression neural network (PSO-GRNN) is established to train the above extracted feature values, and the real blurred image evaluation result will be determined. The proposed method was validated on CID2013 database, which contain real blurred images under different illumination imaging condition. The experimental results showed that the method has good performance in evaluating the quality of images under different imaging conditions.

Keywords-)image sharpness evaluation; different illumination imaging condition; real blur images; PSO-GRNN