Open AccessImproved Gradient Projection Algorithm for Deblurred Image Application
Author(s) -
Yan Huang,
Yanjun Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1575/1/012021
Subject(s) - projection (relational algebra) , computer science , algorithm , image (mathematics) , computer vision , coding (social sciences) , noise (video) , neural coding , artificial intelligence , projection method , code (set theory) , mathematics , statistics , set (abstract data type) , programming language
For the purpose of reducing the noise of deblurred image, an improved Gradient Projection method by sparse coding Reconstruction (GPSR) algorithm is proposed. Different with the traditional Gradient Projection method for sparse reconstruction code (GPSR) method, the obvious distinctions between the original image and deblurred image are projected onto dimensional projection image value. Thus the picture noise are deleted. The classical Gradient Sparse Projection computing method, searching direction changes time by time. Now we propose the new method by running with fix step. The constraints of searching method is guarantee. We prove the new computing method in theory, and at the same time, we running the method by classical picture. We compare the running results of classic method and the fix step hunt method (FXHM), the running results of two methods are shown in the article, the performance of fix step hunt method (FXHM) is higher than the classic method as possible as it can.