传统的超分辨重建算法往往采用梯度下降法进行求解,迭代时步长往往通过经验确定。而且不同的图像的最优步长往往不相同。步长过大会导致发散,步长过小会导致收敛缓慢。本算法基于对正则化超分辨重建算法实现的基础上,对步长的选取进行了优化,推导出了每次迭代时的最优步长大小,并将其自适应化,改进了超分辨算法的收敛性,从而能够在更短的时间内取得更加精确的重建结果。相关具体内容请参考对应的论文:Yingqian Wang, Jungang Yang, Chao Xiao, and Wei An, "Fast convergence strategy for multi-image superresolution via adaptive line search," IEEE Access, vol. 6, no. 1, pp. 9129-9139.
代码片段和文件信息
属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
文件 6779 2017-10-07 11:07 Wang2018Fastcal_ssim.m
文件 3554 2018-05-23 21:20 Wang2018FastDemo_run.m
文件 349 2018-05-23 21:18 Wang2018FastGradient_BTV.m
文件 288 2018-05-23 21:19 Wang2018FastHR2LR.m
文件 478 2018-05-23 21:16 Wang2018FastImDegrate.m
文件 437 2018-05-23 21:17 Wang2018FastImWarp.m
文件 536 2018-05-23 21:18 Wang2018FastL2GradientBackProject.m
文件 724 2018-05-23 21:18 Wang2018Fastline_search.m
文件 341 2018-05-23 21:19 Wang2018FastLR2HR.m
文件 2263 2018-05-23 21:13 Wang2018Fast
eadme.txt
文件 1244214 2013-10-06 16:07 Wang2018FastSet 1.bmp
文件 786486 2013-10-06 16:07 Wang2018FastSet 2.bmp
文件 786486 2013-10-06 16:07 Wang2018FastSet 3.bmp
文件 786486 2013-10-06 16:07 Wang2018FastSet 4.bmp
文件 720054 2013-10-06 16:07 Wang2018FastSet 5.bmp
文件 679830 2017-10-21 07:57 Wang2018FastSet 6.bmp
文件 540054 2017-10-21 07:58 Wang2018FastSet 7.bmp
文件 267894 2017-10-21 19:02 Wang2018FastSet 8.bmp
文件 248886 2013-10-06 16:07 Wang2018FastSet 9.bmp
文件 235350 2013-10-06 16:07 Wang2018FastSet10.bmp
文件 235254 2013-10-06 16:07 Wang2018FastSet11.bmp
文件 196730 2013-10-06 16:07 Wang2018FastSet12.bmp
文件 1179702 2013-10-06 16:07 Wang2018FastSet13.bmp
文件 1039158 2017-11-02 16:55 Wang2018FastSet14.bmp
文件 304182 2013-10-06 16:07 Wang2018FastSet15.bmp
文件 263222 2013-10-06 16:07 Wang2018FastSet16.bmp
文件 304182 2013-10-06 16:07 Wang2018FastSet17.bmp
文件 786486 2013-10-06 16:07 Wang2018FastSet18.bmp
文件 2493 2017-04-15 11:13 Wang2018Fastshift.m
文件 165 2018-05-23 21:18 Wang2018FastTikhonov.m
............此处省略7个文件信息
function ssim = cal_ssim( im1 im2 b_row b_col)
[h w ch] = size( im1 );
ssim = 0;
if (ch == 1)
ssim = ssim_index ( im1(b_row+1:h-b_row b_col+1:w-b_col) im2(b_row+1:h-b_rowb_col+1:w-b_col));
else
for i = 1:ch
ssim = ssim + ssim_index ( im1(b_row+1:h-b_row b_col+1:w-b_col i) im2(b_row+1:h-b_rowb_col+1:w-b_col i));
end
ssim = ssim/3;
end
return
function [mssim ssim_map] = ssim_index(img1 img2 K window L)
% ========================================================================
% SSIM Index with automatic downsampling Version 1.0
% Copyright(c) 2009 Zhou Wang
% All Rights Reserved.
%
% ----------------------------------------------------------------------
% Permission to use copy or modify this software and its documentation
% for educational and research purposes only and without fee is hereby
% granted provided that this copyright notice and the original authors‘
% names appear on all copies and supporting documentation. This program
% shall not be used rewritten or adapted as the basis of a commercial
% software or hardware product without first obtaining permission of the
% authors. The authors make no representations about the suitability of
% this software for any purpose. It is provided “as is“ without express
% or implied warranty.
%----------------------------------------------------------------------
%
% This is an implementation of the algorithm for calculating the
% Structural SIMilarity (SSIM) index between two images
%
% Please refer to the following paper and the website with suggested usage
%
% Z. Wang A. C. Bovik H. R. Sheikh and E. P. Simoncelli “Image
% quality assessment: From error visibility to structural similarity“
% IEEE Transactios on Image Processing vol. 13 no. 4 pp. 600-612
% Apr. 2004.
%
% http://www.ece.uwaterloo.ca/~z70wang/research/ssim/
%
% Note: This program is different from ssim_index.m where no automatic
% downsampling is performed. (downsampling was done in the above paper
% and was described as suggested usage in the above website.)
%
% Kindly report any suggestions or corrections to zhouwang@ieee.org
%
%----------------------------------------------------------------------
%
%Input : (1) img1: the first image being compared
% (2) img2: the second image being compared
% (3) K: constants in the SSIM index formula (see the above
% reference). defualt value: K = [0.01 0.03]
% (4) window: local window for statistics (see the above
% reference). default widnow is Gaussian given by
% window = fspecial(‘gaussian‘ 11 1.5);
% (5) L: dynamic range of the images. default: L = 255
%
%Output: (1) mssim: the mean SSIM index value between 2 images.
% If one of the images being compared is regarded as
% perfect quality then mssim can be considered as the
% quality measure of the other image.
% I
属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
文件 6779 2017-10-07 11:07 Wang2018Fastcal_ssim.m
文件 3554 2018-05-23 21:20 Wang2018FastDemo_run.m
文件 349 2018-05-23 21:18 Wang2018FastGradient_BTV.m
文件 288 2018-05-23 21:19 Wang2018FastHR2LR.m
文件 478 2018-05-23 21:16 Wang2018FastImDegrate.m
文件 437 2018-05-23 21:17 Wang2018FastImWarp.m
文件 536 2018-05-23 21:18 Wang2018FastL2GradientBackProject.m
文件 724 2018-05-23 21:18 Wang2018Fastline_search.m
文件 341 2018-05-23 21:19 Wang2018FastLR2HR.m
文件 2263 2018-05-23 21:13 Wang2018Fast
eadme.txt
文件 1244214 2013-10-06 16:07 Wang2018FastSet 1.bmp
文件 786486 2013-10-06 16:07 Wang2018FastSet 2.bmp
文件 786486 2013-10-06 16:07 Wang2018FastSet 3.bmp
文件 786486 2013-10-06 16:07 Wang2018FastSet 4.bmp
文件 720054 2013-10-06 16:07 Wang2018FastSet 5.bmp
文件 679830 2017-10-21 07:57 Wang2018FastSet 6.bmp
文件 540054 2017-10-21 07:58 Wang2018FastSet 7.bmp
文件 267894 2017-10-21 19:02 Wang2018FastSet 8.bmp
文件 248886 2013-10-06 16:07 Wang2018FastSet 9.bmp
文件 235350 2013-10-06 16:07 Wang2018FastSet10.bmp
文件 235254 2013-10-06 16:07 Wang2018FastSet11.bmp
文件 196730 2013-10-06 16:07 Wang2018FastSet12.bmp
文件 1179702 2013-10-06 16:07 Wang2018FastSet13.bmp
文件 1039158 2017-11-02 16:55 Wang2018FastSet14.bmp
文件 304182 2013-10-06 16:07 Wang2018FastSet15.bmp
文件 263222 2013-10-06 16:07 Wang2018FastSet16.bmp
文件 304182 2013-10-06 16:07 Wang2018FastSet17.bmp
文件 786486 2013-10-06 16:07 Wang2018FastSet18.bmp
文件 2493 2017-04-15 11:13 Wang2018Fastshift.m
文件 165 2018-05-23 21:18 Wang2018FastTikhonov.m
............此处省略7个文件信息
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