BLURRED IMAGE CLASSIFICATION BASED ON ADAPTIVE DICTIONARY

Two frameworks 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 an assumed space invariant Point Spread Function (PSF) estimated from the input blurred image. In one of the proposed two frameworks, the PSF is inferred separately and in the other, the PSF is updated combined with sparse coefficients calculation in an alternative and iterative manner. The experimental results have evaluated three types of blur namely defocus blur, simple motion blur and camera shake blur. The experiment results confirm the effectiveness of the proposed frameworks.