Blurred Image Classification based on Adaptive Dictionary
This addresses image classification under blur for computer vision applications, but it appears incremental as it builds on existing sparse coding and dictionary learning methods.
The paper tackles blurred image classification by proposing two frameworks that use adaptive dictionaries sensitive to the blur's Point Spread Function, avoiding explicit deblurring. The experiments on defocus, motion, and camera shake blurs confirm the frameworks' effectiveness, though no concrete performance numbers are provided.
Two types of framework for blurred image classification based on adaptive dictionary are proposed. Given a blurred image, instead of image deblurring, the semantic category of the image is determined by blur insensitive sparse coefficients calculated depending on an adaptive dictionary. The dictionary is adaptive to the Point Spread Function (PSF) estimated from input blurred image. The PSF is assumed to be space invariant and inferred separately in one framework or updated combining with sparse coefficients calculation in an alternative and iterative algorithm in the other framework. The experiment has evaluated three types of blur, naming defocus blur, simple motion blur and camera shake blur. The experiment results confirm the effectiveness of the proposed frameworks.