【图像处理】小波编码图像中伪影和纹理的检测附Matlab代码和报告-创新互联
⛄ 内容介绍✅作者简介：热爱科研的Matlab仿真开发者，修心和技术同步精进，matlab项目合作可私信。
10年积累的网站制作、成都网站建设经验，可以快速应对客户对网站的新想法和需求。提供各种问题对应的解决方案。让选择我们的客户得到更好、更有力的网络服务。我虽然不认识你，你也不认识我。但先网站设计后付款的网站建设流程，更有武邑免费网站建设让你可以放心的选择与我们合作。
🍎个人主页：Matlab科研工作室
🍊个人信条：格物致知。
更多Matlab仿真内容点击👇
智能优化算法 神经网络预测 雷达通信 无线传感器
信号处理 图像处理 路径规划 元胞自动机 无人机 电力系统
描述了一种小波编码图像的分割和分析算法。该算法构成了图像后处理方案的一部分，该方案可以成功地恢复遭受模糊伪影的压缩图像中的纹理。该算法包括提取纹理、强度（或颜色）和空间特征。使用k均值算法的变体来有效地分割大图像。分析阶段使用基于规则的启发式方法将片段分类为可用于恢复它们的潜在伪影或相邻纹理。这种新颖的图像后处理方法需要最少的用户交互，并且可以成功地利用压缩图像中的纹理级别相关性。
⛄ 部分代码function [dog1, dog2, doog21, doog22, doog23, doog24, doog25, doog26] = doog(hsize);
% Difference of Offset Gaussian and Difference of Gaussian Filters
% for Texture Segmentation.
% Based on work by Jitendra Malik and Pietro Perona, "Preattentive texture
% discrimination with early vision mechanisms," J. Optical Soc. America,
% Vol 7, No 5, May 1990.
% (c) 2003 by Rajas Sambhare
% ECE 738 - Final Project
% Spring 2003
% Common parameters
g = 'gaussian'; % Filter type
% Filter 1. Difference of Gaussian 2
sigmaratio = hsize/8*[0.62, 1, 1.6];
sumratio = -[1, -2, 1];
dog2 = sumratio(1)*fspecial(g, hsize, sigmaratio(1)) + ...
sumratio(2)*fspecial(g, hsize, sigmaratio(2)) + ...
sumratio(3)*fspecial(g, hsize, sigmaratio(3));
% Filter 2. Difference of Gaussian 1
sigmaratio = hsize/8*[0.71, 1, 1.14];
sumratio = [1, -1];
dog1 = sumratio(1)*fspecial(g, hsize, sigmaratio(1)) + ...
sumratio(2)*fspecial(g, hsize, sigmaratio(3));
% Remaining filters
aspect = 3; % Fixed aspect ratio of remaining filters.
% Filter 3.
sigmaratio = [1, 1*aspect];
sumratio = [-1, 2, -1];
doog21 = sumratio(1)*gaussian(0, sigmaratio(1), sigmaratio(1), sigmaratio(2), hsize) + ...
sumratio(2)*gaussian(0, 0, sigmaratio(1), sigmaratio(2), hsize) + ...
sumratio(1)*gaussian(0, -sigmaratio(1), sigmaratio(1), sigmaratio(2), hsize);
% Filter 4. Transpose filter 3 to get filter 4
doog22 = doog21';
% Filter 5, 6, 7, 8. Rotate filter 2 (and flip) %%%TODO renumber doogs%%%
doog23 = imrotate(doog21, -30, 'bicubic', 'crop');
doog24 = imrotate(doog21, -60, 'bicubic', 'crop');
doog25 = flipud(doog23);
doog26 = flipud(doog24);
% Display the filters
% figure,
% subplot(3,3,1), mesh(dog2), title('1');
% subplot(3,3,2), mesh(dog1), title('2');
% subplot(3,3,4), mesh(doog21), title('3');
% subplot(3,3,5), mesh(doog22), title('4');
% subplot(3,3,6), mesh(doog23), title('5');
% subplot(3,3,7), mesh(doog24), title('6');
% subplot(3,3,8), mesh(doog25), title('7');
% subplot(3,3,9), mesh(doog26), title('8');
% %
% figure,
% subplot(3,3,1), imscshow(dog2), title('1');
% subplot(3,3,2), imscshow(dog1), title('2');
% subplot(3,3,4), imscshow(doog21), title('3');
% subplot(3,3,5), imscshow(doog22), title('4');
% subplot(3,3,6), imscshow(doog23), title('5');
% subplot(3,3,7), imscshow(doog24), title('6');
% subplot(3,3,8), imscshow(doog25), title('7');
% subplot(3,3,9), imscshow(doog26), title('8');
% %
% imwrite(rescalegray(dog2), 'dog2.png');
% imwrite(rescalegray(dog1), 'dog1.png');
% imwrite(rescalegray(doog21), 'doog1.png');
% imwrite(rescalegray(doog22), 'doog2.png');
% imwrite(rescalegray(doog23), 'doog3.png');
% imwrite(rescalegray(doog24), 'doog4.png');
% imwrite(rescalegray(doog25), 'doog5.png');
% imwrite(rescalegray(doog26), 'doog6.png');
⛄ 运行结果
[1] Baseline JPEG and JPEG2000 Artifacts Illustrated, Aleks Jakulin, 2002, URI: http://ai.fri.uni-lj.si/~aleks/jpeg/artifacts.html
[2] S. Yang, Y. H. Hu, D. L. Tull, and T. Q. Nguyen, “Maximum likelihood parameter estimation for image ringing artifact removal,” IEEE Trans. Circuits and Systems for Video Technology, vol. 11, no. 8, August, 2001, pp. 963-973.
[3] A. Nosratinia, “Post-Processing of JPEG-2000 Images to Remove Compression Artifacts,” to appear in IEEE Signal Processing Letters.
[4] S. H. Oguz, T. Q. Nguyen, and Y. H. Hu, “Critical quantization decisions in transform coding and blocking artifacts,” Proc. ISCAS'99, Orlando, FL, pp. 63.19, 1999.
[5] S. Yang, Y. H. Hu, and D. L. Tull, “Blocking artifact removal using robust statistics and line process,” IEEE Int'l Workshop on Multimedia Signal Processing, 1999.
[6] S. Yang and Y. H. Hu, “Block Effect Removal using Regularization and Dithering,” Proc. ICIP'98, Chicago, IL, pp. 346-349, 1998.
[7] R. Krishnamurthy, J. W. Wood, and J. M. Francos, “Adaptive restoration of textured images with mixed spectra,” IEEE Trans. Image Processing, vol. 5, pp. 648-652, 1996.
[8] Y. H. Hu and R. Sambhare, “Constrained Texture Synthesis for Image Post Processing”, ICASSP '03, Hong Kong, 2003.
[9] J. Malik and P. Perona, “Preattentive texture discrimination mechanisms with early visio mechanisms”, J. Opt. Soc. Am. A. Vol 7, No. 5, May 1990
[10] R. Duda, P. Hart, and H. Stork, “Pattern Classification,” Wiley-Interscience, New York, NY, 2 ed., 2000.
⛄ Matlab代码关注❤️部分理论引用网络文献，若有侵权联系博主删除
❤️ 关注我领取海量matlab电子书和数学建模资料
你是否还在寻找稳定的海外服务器提供商？创新互联www.cdcxhl.cn海外机房具备T级流量清洗系统配攻击溯源，准确流量调度确保服务器高可用性，企业级服务器适合批量采购，新人活动首月15元起，快前往官网查看详情吧
当前标题：【图像处理】小波编码图像中伪影和纹理的检测附Matlab代码和报告-创新互联
网站网址：http://ybzwz.com/article/dgsdij.html
