http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Image Restoration Based on L1 + L1 Model
Ruihua Liu 보안공학연구지원센터 2014 International Journal of Signal Processing, Image Vol.7 No.5
In this paper, we firstly propose a new image restoration model including non-smooth l1-norm data-fidelity term and non-smooth l1-norm regularization term based on the bilateral total variation regularization. Secondly, we prove the existence of minimal solutions of our proposed energy functional model. Thirdly, we consider the convergence of the discrete numerical algorithm, and obtain that the limit point of the solution sequence is the minimal point of our proposed energy functional. Finally, we give some experimental simulation results in the case of the single noisy image without blurring, multiple different noisy images without blurring, single noisy image with blurring, and multiple different noisy images with different blurring, respectively. The restoration results show our model works effectively.
An Algorithm for image removals and decompositions without inverse matrices
Koninklijke Vlaamse Ingenieursvereniging ; Elsevie 2009 Journal of computational and applied mathematics Vol.225 No.2
Partial Differential Equation (PDE) based methods in image processing have been actively studied in the past few years. One of the effective methods is the method based on a total variation introduced by Rudin, Oshera and Fatemi (ROF) [L.I. Rudin, S. Osher, E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D 60 (1992) 259-268]. This method is a well known edge preserving model and an useful tool for image removals and decompositions. Unfortunately, this method has a nonlinear term in the equation which may yield an inaccurate numerical solution. To overcome the nonlinearity, a fixed point iteration method has been widely used. The nonlinear system based on the total variation is induced from the ROF model and the fixed point iteration method to solve the ROF model is introduced by Dobson and Vogel [D.C. Dobson, C.R. Vogel, Convergence of an iterative method for total variation denoising, SIAM J. Numer. Anal. 34 (5) (1997) 1779-1791]. However, some methods had to compute inverse matrices which led to roundoff error. To address this problem, we developed an efficient method for solving the ROF model. We make a sequence like Richardson's method by using a fixed point iteration to evade the nonlinear equation. This approach does not require the computation of inverse matrices. The main idea is to make a direction vector for reducing the error at each iteration step. In other words, we make the next iteration to reduce the error from the computed error and the direction vector. We describe that our method works well in theory. In numerical experiments, we show the results of the proposed method and compare them with the results by D. Dobson and C. Vogel and then we confirm the superiority of our method.
Image deblurring via adaptive proximal conjugate gradient method
( Han Pan ),( Zhongliang Jing ),( Minzhe Li ),( Peng Dong ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.11
It is not easy to reconstruct the geometrical characteristics of the distorted images captured by the devices. One of the most popular optimization methods is fast iterative shrinkage/ thresholding algorithm. In this paper, to deal with its approximation error and the turbulence of the decrease process, an adaptive proximal conjugate gradient (APCG) framework is proposed. It contains three stages. At first stage, a series of adaptive penalty matrices are generated iterate-to-iterate. Second, to trade off the reconstruction accuracy and the computational complexity of the resulting sub-problem, a practical solution is presented, which is characterized by solving the variable ellipsoidal-norm based sub-problem through exploiting the structure of the problem. Third, a correction step is introduced to improve the estimated accuracy. The numerical experiments of the proposed algorithm, in comparison to the favorable state-of-the-art methods, demonstrate the advantages of the proposed method and its potential.
WANG Xin,MENG Jian,LIU Fu 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.4
In order to solve storage and computation cost problems for the traditional whole sampling image fusion algorithms, a new method of infrared and visible light image fusion is put forward based on compressed sensing (CS) theory. Nonsubsampled shearlet transform (NSST) is introduced as the sparse transform. Compressed sensing is applied to fuse the high frequency subbands decomposed by NSST. The high frequency coefficients are compressed for measured values which are fused by the rules of spatial frequency weighting. Regional energy together with regional standard deviation guides the fusion of the low frequency subband. Finally, the fused image is gained through inverse NSST. The experimental results show that high-quality fused images can be obtained with only one layer NSST. The fused image quality is better than the several traditional fusion algorithms based on compressed sensing.