Current Issue : October - December Volume : 2018 Issue Number : 4 Articles : 5 Articles
Image denoising is an important first step to provide cleaned images for follow-up tasks such as image segmentation\nand object recognition. Many image denoising filters have been proposed, with most of the filters focusing on one\nparticular type of additive or multiplicative noise. In this article, we propose a novel neighborhood regression\napproach. Using the neighboring pixels as predictors, our approach has superb performance over multiple types of\nnoises, including Gaussian, Poisson, Gaussian and Poisson, salt & pepper, and stripped noise. Our L2 regression filter\ncan be parallelized to significantly speed up the denoising process to process a large number of noisy images.\nMeanwhile, our regression approach does not need tuning parameters or any training images, and it does not need\nany prior knowledge of the variance of the noise. Instead, our regression filter can accurately estimate the variance of\nthe added Gaussian noise. We have performed extensive experiments, comparing our regression filter with the\npopular denoising filters, including BM3D, median filter, and wavelet filter, to demonstrate the superb performance of\nour proposed regression filter....
This paper investigates and analyzes the characteristics of video data and puts forward a campus surveillance video storage system\nwith the university campus as the specific application environment. Aiming at the challenge that the content-based video retrieval\nresponse time is too long, the key-frame index subsystem is designed. The key frame of the video can reflect the main content of\nthe video. Extracted from the video, key frames are associated with the metadata information to establish the storage index. The\nkey-frame index is used in lookup operations while querying. This method can greatly reduce the amount of video data reading\nand effectively improves the query�s efficiency. From the above, we model the storage system by a stochastic Petri net (SPN) and\nverify the promotion of query performance by quantitative analysis....
Image quality assessment that aims to evaluate the image quality automatically by a computational model plays a significant role\nin image processing systems. To meet the need of accuracy and effectiveness, in the proposed method, complementary features\nincluding histogram of oriented gradient, edge information, and color information are employed for joint representation of the\nimage quality. Afterwards, the dissimilarities of the extracted features between the distorted and reference images are quantified.\nFinally, support vector regression is used for distortion indices fusion and objective quality mapping. Experimental results validate\nthat the proposed method outperforms the state-of-the-art methods in terms of consistency with subjective perception and\nrobustness across various databases and different distortion types....
Ferrography is a technology that can be applied in inspecting features of wear particles in machines and inferring\ntheir health status. With the development of online ferrography, which employs image processing to captured wear\nparticle images, the inspection process has become automatic. However, it is found that images captured often\ncontain out-of-focus degradations and low brightness. A restoration framework is here proposed to mitigate this\nproblem. The main idea is to extract object edges, magnify with a non-linear gain factor, then combine with the input\nimage to produce an enhanced image to facilitate further analysis. Parameters adopted in the process are optimized\nusing a metaheuristic search where the image information content and brightness are maximized. Experimental\nresults, obtained from processing real-world wear particle images in lubricant circuits, have shown qualitative and\nquantitative improvements over the input images...
Active contour models are widely used in image segmentation. In order to obtain ideal object boundary, researchers utilize various\ninformation to define new models for image segmentation. However, the models could not meet all scenes of image. In this paper,\nwe propose a block evolution method to improve the robustness of contour evolution. A block matrix is consisted of contours of\nformer iterations and contours of shape prior, and a nuclear norm of the matrix is a measure of the similarity of these shapes.\nThe constraint of the nuclear norm minimization is imposed on the evolution of active contour models, which could avoid large\ndeformation of the adjacent curves and keep the shape conformability of contour in the evolution.Theshape prior can be integrated\ninto the block evolution method, which is effective in dealing with missing features of images and noise.The proposed method can\nbe applied to image sequence segmentation. Experiments demonstrate that the proposed method improves the robust performance\nof active contour models and can increase the flexibility of applications in image sequence segmentation....
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