Robust texture features for blurred images using Undecimated Dual-Tree Complex Wavelets

This paper presents a new descriptor for texture classification. The descriptor is rotationally invariant and blur insensitive, which provides great benefits for various applications that suffer from out-of-focus content or involve fast moving or shaking cameras. We employ an Undecimated Dual-Tree Complex Wavelet Transform (UDT-CWT) [1] to extract texture features. As the UDT-CWT fully provides local spatial relationship between scales and subband orientations, we can straightforwardly create bit-planes of the images representing local phases of wavelet coefficients. We also discard some of the finest decomposition levels where are most affected by the blur. A histogram of the resulting code words is created and used as features in texture classification. Experimental results show that our approach outperforms existing methods by up to 40% for synthetic blurs and up to 30% for natural video content due to camera motion when walking.