Blur image detection using Laplacian operator and Open-CV

With the increased usage of digital cameras and picture clicking devices, the number of digital images increases rapidly, which in return demand for image quality assessment in terms of blur. Based on the edge type and sharpness analysis using Laplacian operator, an effective representation of blur image detection scheme is proposed in this paper, which can determine that whether the image is blurred or not, and what is the extent of blur through Variance of Laplacian. In this project a simple, reliable and fast algorithm for image noise estimation is presented. Images taken for the testing is assumed to be corrupted by additive zero mean Gaussian noise. To exclude structures or details from contributive to the noise variance estimation, an easy edge detection algorithm inculcating first-order gradients is applied first. Then a Laplacian operator followed by an averaging over the whole image will provide very accurate noise variance estimation. There is only one parameter which is self-determined and adaptive to the image contents. Simulation results show that the proposed algorithm performs well for different types of images over a large range of noise variances. Performance comparisons against alternative approaches are also provided. Results obtain through screenshots of the experiments demonstrate the effectiveness of the proposed scheme.