In this paper, we propose a partially-blurred-image classification and analysis framework for automatically detecting images containing blurred regions and recognizing the blur types for those regions without needing to perform blur kernel estimation and image deblurring. We develop several blur features modeled by image color, gradient, and spectrum information, and use feature parameter training to robustly classify blurred images. Our blur detection is based on image patches, making region-wise training and classification in one image efficient. Extensive experiments show that our method works satisfactorily on challenging image data, which establishes a technical foundation for solving several computer vision problems, such as motion analysis and image restoration, using the blur information.

In image deconvolution, the boundary value problem, if not appropriately handled, often causes serious ringing artifacts in the restored results. This paper proposes a simple method to tackle this problem without any assumption on the noise level and the symmetry of the Point Spread Function (PSF). We establish new boundary conditions by smoothly expanding the input image to a large tile. It helps reducing the boundary discontinuities and accordingly makes all restoration filters based on Fast Fourier Transform (FFT) not produce obvious image border artifacts.