Image Blur Classification and Parameter Identification using Two-stage Deep Belief Networks

Image blur kernel classification and parameter estimation are critical for blind image deblurring. Current dominant approaches use handcrafted blur features that are optimized for a certain type of blur, which is not applicable in real blind deconvolution application when the Point Spread Function (PSF) of the blur is unknown. In this paper, a Two-stage system using Deep Belief Networks (TDBN) is proposed to first classify the blur type and then identify its parameters. To the best of our knowledge, this is the first time that Deep Belief Network (DBN) has been applied to the problem of blur analysis. In the blur type classification, our method attempts to identify the blur type from mixed input of various blurs with different parameters, rather than blur estimation based on the assumption of a single blur type in current methodology. To this aim, a semi-supervised DBN is trained to project the input samples in a discriminative feature space, and then classify those features. Moreover, in the parameter identification, the proposed edge detection on logarithm spectrum helps DBN to identify the blur parameters with very high accuracy. Experiments demonstrate the effectiveness of the proposed methods with better results compared to the state-of-the-art on the Berkeley segmentation dataset and the Pascal VOC 2007 dataset.