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Gradient Histogram Estimation And Preservation For Texture Enhanced Image Denoising

Natural Image Statistics Plays An Important Role In Image Denoising, And Various Natural Image Priors, Including Gradient-based, Sparse Representation-based, And Nonlocal Self Similarity-based Ones, Have Been Widely Studied And Exploited For Noise Removal. In Spite Of The Great Success Of Many Denoising Algorithms, They Tend To Smooth The Fine Scale Image Textures When Removing Noise, Degrading The Image Visual Quality. To Address This Problem, In This Paper, We Propose A Texture Enhanced Image Denoising Method By Enforcing The Gradient Histogram Of The Denoised Image To Be Close To A Reference Gradient Histogram Of The Original Image.

Finger Print Combination For Privacy Protection

The Proposed Novel System For Protecting Finger Print Privacy By Combining Two Different Fingerprints Into A New Identity. In The Enrollment, Two Fingerprints Are Captured From Two Different Fingers. We Extract The Minutiae Positions From One Finger Print, The Orientation From The Other Fingerprint, And The Reference Points From Both Fingerprints. Based On This Extracted Information And Our Proposed Coding Strategies, A Combined Minutiae Template Is Generated And Stored In A Database. In The Authentication, The System Requires Two Query Fingerprints From The Same Two Fingers Which Are Used In The Enrollment. A Two-stage Finger Print Matching Process Is Proposed For Matching The Two Query Finger Prints Against A Combined Minutiae Template.

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