SINGLE IMAGE DEBLURRING WITH OR WITHOUT FACE PRIOR AND ITS APPLICATIONS Single Image deblurring with or without face prior and its applications
The motion blur is one of the most difficult challenges in photography, which is generated from the relative motion between the sensor and the scene during exposure time. These blur artifacts degrade the visual experience, and the performance of various applications, such as, object detection, facial analysis. Therefore, it is significant to remove the blur and restore sharp and clean images. Our work focuses on the general single image deblurring, and face image deblurring with face prior.
... ith face prior. State-of-the-art single image deblurring techniques are sensitive to image noise. Even a small amount of noise, which is inevitable in low-light conditions, can degrade the quality of blur kernel estimation dramatically. We propose a new method for handling noise in blind image deconvolution based on new theoretical and practical insights. Based on the observations on directional filter, our method applies a series of directional filters at different orientations to the input image, and estimates an accurate Radon transform of the blur kernel from each filtered image. Finally, we reconstruct the blur kernel using inverse Radon transform. Experimental results on synthetic and real data show that our algorithm achieves higher quality results than previous approaches on blurry and noisy images. The human face is one of the most essential focuses in numerous applications. Although ii significant progress has been made in the image deblurring area, few of them can obtain promising results on blurry face images. Many state-of-the-art single image deblurring approaches estimate the blur kernel based on analyzing the edge profiles of the input image. However, the detection of strong edges is very difficult on human faces, since the human faces do not contain as much texture as natural images. We propose to utilize the global face structure information to help with the strong or salient edge detection. Our method outperforms the existing methods in extensive evaluations on synthetic and real face images. Facial expression is a significant application on sharp and restored face images. To improve the general facial expression recognition performance, we present a new idea to analyze facial expression by exploring the common and specific information among different expressions. iii