This paper suggests elegant two enhancement approaches for rib chest images. The first approach is based on adaptive contrast and luminance model (ACLM).The second approach is depended on mixing the Exponential Contrast Limited Adaptive Histogram Equalization model (ECLAHE) with the Local Histogram Equalization (LHE). The idea of this approach is depended on applying on rib chest radiograph and make optimization for clip limit for ECLAHE. This second algorithm has helped rib chest radiograph details are more important for the detection of cancerous cells. The performance qualities of the suggested models are entropy, average gradient, contrast factor, Sobel magnitude, lightness order error and the similarity of edges point of views. The second approach presents enhancement of rib chest images with better resolution visual details and quality metrics point of views with comparing the first approach.
相似文献This paper presents a super-resolution (SR) technique for enhancement of infrared (IR) images. The suggested technique relies on the image acquisition model, which benefits from the sparse representations of low-resolution (LR) and high-resolution (HR) patches of the IR images. It uses bicubic interpolation and minimum mean square error (MMSE) estimation in the prediction of the HR image with a scheme that can be interpreted as a feed-forward neural network. The suggested algorithm to overcome the problem of having only LR images due to hardware limitations is represented with a big data processing model. The performance of the suggested technique is compared with that of the standard regularized image interpolation technique as well as an adaptive block-by-block least-squares (LS) interpolation technique from the peak signal-to-noise ratio (PSNR) perspective. Numerical results reveal the superiority of the proposed SR technique.
相似文献This framework presents three efficient proposed algorithms for pedestrian detection and tracking in Dark Infrared Night Vision (DIRNV) images. The first approach is relied on Gradient Estimation (GE) after mixing structure Equalization Exponential Contrast Limited Adaptive Histogram Equalization (ECLAHE) with Gamma Correction, and finally Cumulative Histogram (GECUGC) for discrimination. The GECUGC relies on enhancement using mixing ECLAHE Using Gamma Correction (ECUG) in addition to pre-processing followed by the GE using Laplacian Filter (LAF), and finally Cumulative Histograms (CH) for the detection or classification task. The second approach is based GE after a hybrid structure Histogram Equalization (HE) with Nonlinear Technique and finally CH (GHNTC) for discrimination. The GHNTC depends on enhancement by merging HE with Nonlinear Technique (NT) (HENT) followed by the GE using LAF and finally CH for pedestrian detection and tracking using DIRNV imaging. After the CH estimation, the difference between cumulative histograms with and without objects is estimated and used for pedestrian detection and tracking using DIRNV imaging. The third algorithm is based scale space analysis with the number of the Speeded Up Robust Features (SURF) points as the key parameters for classification. This technique is presented to detect the features of DIRNV pedestrian images and tracking. The performance metrics are the difference area between the cumulative histograms of DIRNV images with and without pedestrian, computation time, points of features and speed up factor. Simulation results prove that the success of three suggested techniques in pedestrian detection and tracking using DIRNV imaging. By comparing the three presented algorithms, it is clear that the second suggested technique gives superior for pedestrian detection and tracking from point view difference area between the cumulative histograms.On the other hand the first suggested technique is the best algorithms for pedestrian detection and tracking from point view the computation time. The obtained results clear that the third approach has sucesseded in gait pedestrian detection and tracking using DIRNV imaging.
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