Abstract
The shadow of pavement images will affect the accuracy of road crack recognition and increase the rate of error detection. A shadow separation algorithm based on morphological component analysis (MCA) is proposed herein to solve the shadow problem of road imaging. The main assumption of MCA is that the image geometric structure and texture structure components are sparse within a class under a specific base or overcomplete dictionary, while the base or overcomplete dictionaries of each sparse representation of morphological components are incoherent. Thereafter, the corresponding image signal is transformed according to the dictionary to obtain the sparse representation coefficients of each part of the information, and the coefficients are shrunk by soft thresholding to obtain new coefficients. Experimental results show the effectiveness of the shadow separation method proposed in this paper.
Highlights
With the development of image processing and signal technology, how to use the components of the signal and image such as subcomponent, principal component, independent component, sparse component, and morphological component to represent the image and signal has become a research focus of many signal and image processing tasks, such as reconstruction, noise suppression, compression, and feature extraction
In this work, nonsampled curvelet transform (NSCT) enhancement algorithm is used to enhance and transform the road crack graph with shadow in Figure 4(a), and two direction subband graphs of the third layer shown in Figure 4(b) are obtained
It can adaptively approximate the shadow and image background in the road image, so as to provide a high-quality input image that meets the requirements of high-level image understanding for image segmentation, target recognition, and other tasks as an image preprocessing step. e proposed algorithm involves sparse representation theory and morphological component analysis (MCA)
Summary
With the development of image processing and signal technology, how to use the components of the signal and image such as subcomponent, principal component, independent component, sparse component, and morphological component to represent the image and signal has become a research focus of many signal and image processing tasks, such as reconstruction, noise suppression, compression, and feature extraction. MCA is used to realize signal separation in several fields such as firstorder and second-order cyclostationary signal separation [3], to enhance textural differences based on wavelet texture features to improve the image segmentation preprocessing method [4], to decompose oscillation plus the transient signal [5], to decompose the interference hyperspectral image [6], double-layer adaptive shape morphological analysis for retinal image evaluation [7], and to separate different types of noise in seismic image processing [8,9,10,11] All these show that MCA is effective in signal separation. (2) is work provides a high-quality input image that meets the requirements of high-level image understanding for image segmentation, target recognition, and other tasks as an image preprocessing step
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
