Image processing has gained an increased usage and impact in modern pavement networks automatic distress severity classification (DSC). DSC defines priorities and maintenance resources optimum allocation in order to achieve a cost-effective rehabilitation process. This paper presents a novel computer vision algorithm having the ability to process, isolate and evaluate the distress severity level of a pavement. A pavement color image is converted to grayscale and then processed for image denoising of the granularity and complex texture that represent and artifact in cracks edge detection. The processing is achieved by a 2D dual-tree double density wavelet transform filter banks that significantly reduces the granularity noise while preserving the pavement cracks for edge detection. The 2D wavelet FIR filters perform analysis, soft thresholding then a synthesis of the image. The second step is then an edge detection process followed by morphological filtering and labeled components size-histogram filter to isolate false edges as residuals of denoising. A final step is performed by two Savitzky-Golay filters for the detection of longitudinal and transverse alligator cracks projections. A weighted score function with multiple parameters is used for DSC.