Finger vein image deblurring reconstruction based on Polak-Ribière conjugate Gradient Projection

In this paper, we presents a compressive sensing (CS) based application --Polak-Ribière conjugate gradient projection (PR-CGP) for solving bound constrained quadratic program to reduce noise in synthetic vein images and real finger vein images respectively which blurred with various noise. Then compares the result with the reconstruction by Gradient Projection for Sparse Restruction (GPSR) algorithm. The results show that the method has better performance than the GPSR method in reducing noise, and thus provides more accurate information for vein recognition and extraction.