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- W3159475199 abstract "Image improvement is one among the issues in low visual quality and low enhancement level of image histogram method. Image improvement completely different methods like histogram leveling, multipoint histogram equalizations and picture component dependent distinction protecting, but of this technique are not up to marks. Projected linear perception network methodology for image improvement that features an additional strong result for distinction improvement with brightness preservation. Image element mutuality linear perceptron network supported curvelet transform and perceptron network. Curvelet transform image transform into multi-resolution mode. it's a realize element distinction of pixel for the dependency of characteristic and matrix work as a weight vector for perceptron network and so the perceptron network is in work to alter the load of input image or values. Image mutuality linear perceptron network for distinction improvement has applied on several pictures and compared the results of our proposed methodology with various image improvement methods like histogram leveling. Absolute mean brightness error (MBE) is used to measure the degree of brightness preservation. Low AMBE and also called is best, Peak signal to noise quantitative relation (PSNR) is used to measure the degree of distinction improvement, larger PSNR and also called is best. In experiment image secure encoding improvement technique using histogram leveling with proposed methodology supported the MBE low and PSNR High. Image secure cryptography improvement have found that proposed methodology is best than existing methodology (HEM).Keywords: Contrast Enhancement, Image Histogram, Histogram Equalization, Brightness Preserving, AMBE, PSNR, HEM." @default.
- W3159475199 created "2021-05-10" @default.
- W3159475199 creator A5073007837 @default.
- W3159475199 date "2020-12-01" @default.
- W3159475199 modified "2023-09-23" @default.
- W3159475199 title "Digital Image Visual Quality Enhancement Using Histogram Equalization Techniques with Proposed Linear Perception Neural Network Method" @default.
- W3159475199 hasPublicationYear "2020" @default.
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