Blurred Image Classification based on Adaptive Dictionary

TL;DR

This paper introduces two adaptive dictionary-based frameworks for classifying blurred images by determining their semantic categories without deblurring, using blur-insensitive sparse coefficients and an estimated PSF.

Contribution

It proposes novel adaptive dictionary methods that classify blurred images directly, avoiding traditional deblurring, and introduces iterative algorithms for PSF estimation and sparse coefficient calculation.

Findings

Effective classification across defocus, motion, and shake blurs

Adaptive dictionaries improve classification accuracy

Frameworks outperform traditional methods

Abstract

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.