Quality assessment for real out-of-focus blurred images

During the process of image acquisition, images are often subject to out-of-focus or defocus blur because of the improper adjustment of the camera's focal length, this image blur will degrade the image quality. However, in the literature, image quality assessment (IQA) methods dedicated to evaluating the quality of images with out-of-focus blur remain few. Therefore, in this paper, we focus our attention on the quality assessment of images that suffer from out-of-focus blur and propose an objective quality assessment method accordingly. Concretely, we construct a dedicated out-of-focus blurred image dataset, which is composed of 150 images subjected to different degrees of out-of-focus blur and the mean opinion scores (MOSs). Then, we propose a specific objective quality metric for the blurred images, which combines image sharpness assessment and saliency-guided pooling strategy. Experimental results demonstrate the proposed metric highly correlates with human judgements of image quality.