Adaptive threshold denoising of regularized super-resolution reconstruction procedure
In order to enhance the reconstruction ability of regularized super-resolution technique for noisy image,an adaptive threshold denoising method was proposed based on the extended research of General Total Variation (GTV) regularized super-resolution reconstruction.Firstly,the iterative reconstruction was completed according to GTV regularized super-resolution reconstruction.Then,the deduced adaptive threshold matrix was used to divide GTV cost matrix of each iteration procedure by the threshold.The corresponding pixel points whose costs were less than the threshold continued to be iterated while the points whose costs were greater than the threshold were cut down for re-interpolating and canceled from the iteration of this turn.Finally,the reconstruction result was output when the program met the convergence requirement.The experimental results show that,compared with the single GTV regularized reconstruction method and adaptive parameter method,the proposed adaptive threshold denoising method accelerates the convergence rate and improves the quality of reconstruction image,which makes the regularized super-resolution reconstruction technology perform better for noisy image.
super-resolution reconstructionregularization techniqueGeneral Total Variation (GTV)adaptive thresholdimage denoising
NETL
NSTL
万方数据
维普
