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Motion blur detecting by support vector machine

Among various factors degrading captured image quality, motion blur is the most common. The user becomes aware of the blur only after viewing pictures on a higher resolution display. The causes of motion blur are object movement, camera shake, or any relative speed between the object and the camera. To avoid this problem, many anti-shaking or image stabilization techniques have been developed. However, a detecting mechanism for motion blur is still lacking. Hence, this paper will address some possible solutions and evaluate their performance. The purpose of a motion blur detector is to classify the digital image as blurred or clear and inform users. This function can supply information for users to decide to retake the picture immediately instead of turning the camera to playback mode to check. For achieving higher error tolerance and adaptation to different image capturing circumstances, a machine learning technique is employed. Different digital image processing schemes are explored to find the most discriminative features. Among the many machine techniques, Support Vector Machine (SVM) has been implemented. To achieve the best performance for SVM, inherent information extraction from motion blurred images is extremely important. Thus, several signal transformations including discrete Fourier, discrete cosine, and Radon transformation have been explored. A comparison of the performance of different feature vectors, kernel function, and parameters will also been addressed in this paper.