Multiridgelets for texture analysis

The directional wavelet used in image processing has orientation selectivity and can provide a sparse representation of edges in natural images. Multiwavelets offer the possibility of better performance in image processing applications as compared to the scalar wavelet. Applying directionality to multiwavelets may thus gain both advantages. This paper proposes a scheme, named multiridgelets, which is an extension of ridgelets. We consider the application of the balanced multiwavelet transform to the Radon transform of an image. Specifically, we consider its use in the image texture analysis. The regular polar angle method is employed to realize the discrete transform. Three statistical features: standard deviation, median and entropy are computed based on multiridgelet coefficients. Classification of the mura defects of the LCD screen is tested to quantify the performance of the proposed texture analysis methods. 240 normal images and 240 simulated defective images are supplied to train a support vector machine classifier, and another set of 40 normal and 40 defective images for testing. A comparative study was made with the results obtained using 2D wavelet, scalar ridgelet, and curvelet methods. We conclude that multiridgelet method was comparable to or better than curvelet method and gave significantly better performance than 2D wavelet and scalar ridgelet methods.