No-reference blur estimation based on the average cone ratio in the wavelet domain


With extensive technological advancements in electronic imaging today, high image quality is becoming an imperative necessity in the modern imaging systems. An important part of quality assurance are techniques for measuring the level of image distortion. Recently, we proposed a wavelet based metric of blurriness in the digital images named CogACR. The metric is highly robust to noise and able to distinguish between a great range of blurriness. Also, it can be used either when the reference degradation-free image is available or when it is unknown. However, the metric is content sensitive and thus in a no-reference scenario it was not fully automated. In this paper, we further investigate this problem. First, we propose a method to classify images based on edge content similarity. Next, we use this method to automate the CogACR estimation of blur in a no-reference scenario. Our results indicate high accuracy of the method for a range of natural scene images distorted with the out-of-focus blur. Within the considered range of blur radius of 0 to 10 pixels, varied in steps of 0.25 pixels, the proposed method estimates the blur radius with an absolute error of up to 1 pixel in 80 to 90% of the images.