Defocus Blur Segmentation Using Genetic Programming and Adaptive Threshold

Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type, scenarios and level of blurriness. In this paper, we propose an effective method for blur detection and segmentation based on transfer learning concept. The proposed method consists of two separate steps. In the first step, genetic programming (GP) model is developed that quantify the amount of blur for each pixel in the image. The GP model method uses the multi-resolution features of the image and it provides an improved blur map. In the second phase, the blur map is segmented into blurred and non-blurred regions by using an adaptive threshold. A model based on support vector machine (SVM) is developed to compute adaptive threshold for the input blur map. The performance of the proposed method is evaluated using two different datasets and compared with various state-of-the-art methods. The comparative analysis reveals that the proposed method performs better against the state-of-the-art techniques.