In patch-based inpainting methods, the order of filling the areas to be restored is very important. This filling order is defined by a priority function that integrates two parameters: confidence term and data term. The priority, as initially defined, is negatively affected by the mutual influence of confidence and data terms. In addition, the rapid decrease to zero of confidence term leads the numerical instability of algorithms. Finally, the data term depends only on the central pixel of the patch, without taking into account the influence of neighboring pixels. Our aim in this paper is to propose an algorithm to solve the problems mentioned above. This algorithm is based on a new definition of the priority function, a calculation of the average data term obtained from the elementary data terms in a patch and an update of the confidence term slowing its decrease and avoiding convergence to zero. We evaluated our method by comparing it with algorithms in the literature. The results show that our method provides better results both visually and in terms of the Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity index (SSIM).
The Telegraph Diffusion Equation (TDE) used in some noise reduction processes in an image includes two main parameters: the damping coefficient and the relaxation time. Classically, the first is determined globally for a given input image, while the second one is set constant. In this paper, we propose to determine the values of these parameters according to the information and the image local structure. We then get an adaptive diffusion equation that permits to better control the degree of smoothness and preserve fine structures and image contours avoiding speckles phenomena and staircase. The acquired results show that the proposed method improves the quality of images that have a weak signal-to-noise ratio, comparatively to the methods based on the TDE whose parameters are not adaptive.
In many applications, the specular highlight presence is disruptive information. Its removal strongly depends on the effectiveness of its detection. In this paper, we propose a new method for detecting specular highlight. This method is generic and is based on automatic thresholding. The determination of the threshold is done algorithmically. It is based on the exploitation of the L* component of the CIELab colour space and the exponential function. The exponential function creates a gap between the brightest pixels and the other pixels. The experimental results show the effectiveness of our detection method on images of different nature.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.