The extraction of pavement cracks is always a hard task in image processing. In airport and road construction, cracking is the main factor for pavement damage, which can decrease the quality of pavement and affect transportation seriously. Cracks also exist in other artificial or natural objects, such as buildings, bridges, tunnels, etc. Among all the object images, pavement crack images are the most complex, so the image processing and analysis for them is harder than other crack images. From the early image acquisition based on photography technology to the current 3D laser scanning technology, the pavement crack image acquisition technology is becoming more convenient and efficient, but there are still challenges in the automatic processing and recognition of cracks in images. From the early global thresholding to deep learning algorithms, the research for crack extraction has been developed for about 40 years. There are many methods and algorithms that are satisfactory in pavement crack applications, but there is no standard until today. Therefore, in order to know the developing history and the advanced research, we have collected a number of literature in this research topic for summarizing the research artwork status, and giving a review of the pavement crack image acquisition methods and 2D crack extraction algorithms. Also, for image acquisition methods and pavement crack image segmentation, more detailed comparison and discussions are made.

Pavement crack image acquisition methods and crack extraction algorithms: A review

Optimal Wiener and homomorphic filtration

In order to obtain more precise information regarding rock fractures, images of a rock sample using two different light sources were acquired and fused. For image acquisition, an epoxy resin liquid was first injected into a fracture zone in situ, and when the epoxy resin was dry, the rock core sample, including the epoxy resin, was removed from the rock base. The rock core sample was then cut into multiple slices and images of the slices using ultraviolet (UV) and visible lighting were acquired. In order to fuse two slice images, an algorithm based on the redundant lifting, non-separable wavelet transform was studied and utilised. Fusion includes three primary steps for each pair of slice images: (1) applying the redundant lifting, non-separable wavelet transform to each image, and then approximating the two images separately; (2) fusing the approximated images corresponding to the decomposition level using certain rules and fusion operators for obtaining fusion coefficients; and (3) applying the fused wavelet coefficients to the redundant lifting non-separable wavelet transform. The results show that combining the proposed method of image acquisition and the image fusion algorithm is not only effective at obtaining a large volume of detailed rock fracture information, it is also an economical and easy to use method. By applying the new method, rock fractures can be easily detected, and many different parameters in different rock engineering applications can be measured and analyzed.

Rock fracture image acquisition using two kinds of lighting and fusion on a wavelet transform

Image restoration is the process of restoring the original image from a degraded one. Images can be affected by various types of noise, such as Gaussian noise, impulse noise, and affected by blurring, which is happened during image recordings like motion blur, Out-of-Focus Blur, and others. Image restoration techniques are used to reverse the effect of noise and blurring. Restoration of distorted images can be done using some information about noise and the blurring nature or without any knowledge about the image degradation process. Researchers have proposed many algorithms in this regard; in this paper, different noise and degradation models and restoration methods will be discussed and review some researches in this field.

Advance in Image and Audio Restoration and their Assessments: A Review

With the increase demand of better image quality lot of image processing algorithms are designed. In this paper we described a method to remove the motion blur present in the image taken form any cameras. Motion blur is the result of the relative motion between the camera and the scene during image exposure time, this includes both camera and scene objects motion.. The aim of image restoration is to reconstruct or estimate an uncorrupted image by using the degraded version of the same image. [2] The most closely related research can be found in Blind deconvolution, and Wiener filter methods, They adopts regularized iteration to restore the degraded image. This work proposes the implementation of Wiener filter with Image fusion to reduce the computational complexity with better acceptable restoration results of image restoration method.. The performance of the every stage is tabulated for the parameters like SNR and RMSE of the restored images.

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Arun KumarPatel

Papers

Method for Image Restoration using Wavelet based Image Fusion