% =========================================================================
% Test code for Super-Resolution Convolutional Neural Networks (SRCNN)
%
% Reference
% Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. Learning a Deep Convolutional Network for Image Super-Resolution,
% in Proceedings of European Conference on Computer Vision (ECCV),
%
% Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. Image Super-Resolution Using Deep Convolutional Networks,
% arXiv:1501.00092
%
% Chao Dong
% IE Department, The Chinese University of Hong Kong
% For any question, send email to ndc.forward@gmail.com
% ========================================================================= close all;
clear all; %% read ground truth image
im = imread('Set5\butterfly_GT.bmp');
%im = imread('Set14\zebra.bmp'); %% set parameters
up_scale = ;
model = 'model\9-5-5(ImageNet)\x3.mat';
% up_scale = ;
% model = 'model\9-3-5(ImageNet)\x3.mat';
% up_scale = ;
% model = 'model\9-1-5(91 images)\x3.mat';
% up_scale = ;
% model = 'model\9-5-5(ImageNet)\x2.mat';
% up_scale = ;
% model = 'model\9-5-5(ImageNet)\x4.mat'; %% work on illuminance only
if size(im,)>
im = rgb2ycbcr(im);
im = im(:, :, );
end
im_gnd = modcrop(im, up_scale); %保证图像被up_scale整除
im_gnd = single(im_gnd)/; %Single(单精度浮点型)变量存储为 IEEE 位( 个字节)浮点数值的形式,它的范围在负数的时候是从 -3.402823E38 到 -1.401298E-45,而在正数的时候是从 1.401298E-45 到 3.402823E38。 %% bicubic interpolation
im_l = imresize(im_gnd, /up_scale, 'bicubic'); %缩小3倍
im_b = imresize(im_l, up_scale, 'bicubic'); %又放大三倍 %% SRCNN
im_h = SRCNN(model, im_b); %用网络处理一下 %% remove border %去除没有用的边界
im_h = shave(uint8(im_h * ), [up_scale, up_scale]); %表示变量是无符号整数,范围是0到255.
im_gnd = shave(uint8(im_gnd * ), [up_scale, up_scale]);
im_b = shave(uint8(im_b * ), [up_scale, up_scale]); %% compute PSNR
psnr_bic = compute_psnr(im_gnd,im_b);
psnr_srcnn = compute_psnr(im_gnd,im_h); %% show results
fprintf('PSNR for Bicubic Interpolation: %f dB\n', psnr_bic);
fprintf('PSNR for SRCNN Reconstruction: %f dB\n', psnr_srcnn); %保存 图片
imwrite(im_h,'img_h.png');
imwrite(im_b,'img_b.png');
imwrite(im_gnd,'img_gnd.png'); figure, imshow(im_b); title('Bicubic Interpolation');
figure, imshow(im_h); title('SRCNN Reconstruction'); %imwrite(im_b, ['Bicubic Interpolation' '.bmp']);
%imwrite(im_h, ['SRCNN Reconstruction' '.bmp']);

SRCNN的核心算法:

function im_h = SRCNN(model, im_b)

%% load CNN model parameters
load(model);
[conv1_patchsize2,conv1_filters] = size(weights_conv1);
conv1_patchsize = sqrt(conv1_patchsize2);
[conv2_channels,conv2_patchsize2,conv2_filters] = size(weights_conv2);
conv2_patchsize = sqrt(conv2_patchsize2);
[conv3_channels,conv3_patchsize2] = size(weights_conv3);
conv3_patchsize = sqrt(conv3_patchsize2);
[hei, wid] = size(im_b); %% conv1
weights_conv1 = reshape(weights_conv1, conv1_patchsize, conv1_patchsize, conv1_filters);
conv1_data = zeros(hei, wid, conv1_filters);
for i = : conv1_filters
conv1_data(:,:,i) = imfilter(im_b, weights_conv1(:,:,i), 'same', 'replicate');
conv1_data(:,:,i) = max(conv1_data(:,:,i) + biases_conv1(i), );
end %% conv2
conv2_data = zeros(hei, wid, conv2_filters);
for i = : conv2_filters
for j = : conv2_channels
conv2_subfilter = reshape(weights_conv2(j,:,i), conv2_patchsize, conv2_patchsize);
conv2_data(:,:,i) = conv2_data(:,:,i) + imfilter(conv1_data(:,:,j), conv2_subfilter, 'same', 'replicate');
end
conv2_data(:,:,i) = max(conv2_data(:,:,i) + biases_conv2(i), );
end %% conv3
conv3_data = zeros(hei, wid);
for i = : conv3_channels
conv3_subfilter = reshape(weights_conv3(i,:), conv3_patchsize, conv3_patchsize);
conv3_data(:,:) = conv3_data(:,:) + imfilter(conv2_data(:,:,i), conv3_subfilter, 'same', 'replicate');
end %% SRCNN reconstruction
im_h = conv3_data(:,:) + biases_conv3;

图解里面变量和卷积

SRcnn:神经网络重建图片的开山之作的更多相关文章

  1. 这部分布式事务开山之作,凭啥第一天预售就拿下当当新书榜No.1?

    大家好,我是冰河~~ 今天,咱们就暂时不聊[精通高并发系列]了,今天插播一下分布式事务,为啥?因为冰河联合猫大人共同创作的分布式事务领域的开山之作--<深入理解分布式事务:原理与实战>一书 ...

  2. 【神经网络与深度学习】【计算机视觉】RCNN- 将CNN引入目标检测的开山之作

    转自:https://zhuanlan.zhihu.com/p/23006190?refer=xiaoleimlnote 前面一直在写传统机器学习.从本篇开始写一写 深度学习的内容. 可能需要一定的神 ...

  3. 吴裕雄 python神经网络 水果图片识别(3)

    import osimport kerasimport timeimport numpy as npimport tensorflow as tffrom random import shufflef ...

  4. 论文翻译——R-CNN(目标检测开山之作)

    R-CNN论文翻译 <Rich feature hierarchies for accurate object detection and semantic segmentation> 用 ...

  5. 深度学习(pytorch)-1.基于简单神经网络的图片自动分类

    这是pytorch官方的一个例子 官方教程地址:http://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-b ...

  6. 深度学习原理与框架-Tensorflow卷积神经网络-cifar10图片分类(代码) 1.tf.nn.lrn(局部响应归一化操作) 2.random.sample(在列表中随机选值) 3.tf.one_hot(对标签进行one_hot编码)

    1.tf.nn.lrn(pool_h1, 4, bias=1.0, alpha=0.001/9.0, beta=0.75) # 局部响应归一化,使用相同位置的前后的filter进行响应归一化操作 参数 ...

  7. 吴裕雄 python神经网络 花朵图片识别(10)

    import osimport numpy as npimport matplotlib.pyplot as pltfrom PIL import Image, ImageChopsfrom skim ...

  8. 吴裕雄 python神经网络 花朵图片识别(9)

    import osimport numpy as npimport matplotlib.pyplot as pltfrom PIL import Image, ImageChopsfrom skim ...

  9. 吴裕雄 python神经网络 水果图片识别(4)

    # coding: utf-8 # In[1]:import osimport numpy as npfrom skimage import color, data, transform, io # ...

随机推荐

  1. 微信小程序--分享界面自定义图片

    一般小程序页面都会大于等于1页,每个页面右上角都会有分享功能,建议把以下方法封装到app.js文件,在页面直接调用该方法,避免重复代码,提高性能.(代码用到ES6语法,若不支持,请自行还原js) // ...

  2. Tomcat启动慢原因之一 At least one JAR was scanned for TLDs yet contained no TLDs

    Tomcat启动时提示: 信息: At least one JAR was scanned for TLDs yet contained no TLDs. Enable debug logging f ...

  3. react组件直接在document上添加事件

    demo:比如组件里有个div写的框框,点击document body的背景色变红,点击div写的框框没效果 componentDidMount(){ document.onclick = this. ...

  4. 献给java求职路上的你们

    为了更好的树立知识体系,我附加了相关的思维导图,分为pdf版和mindnote版.比如java相关的导图如下: 由于时间仓促,有些地方未写完,后面会继续补充.如有不妥之处,欢迎及时与我沟通. 相关概念 ...

  5. 【vue】webpack插件svg-sprite-loader---实现自己的icon组件

    引言:最近开始写vue的项目,借鉴了一下vue-element-admin源码,针对vue有一个关于icon图标的处理,最近也找了很多关于vue的icon处理的解决方案,大部分都是按照之前小程序的方式 ...

  6. Pwn with File结构体(三)

    前言 本文由 本人 首发于 先知安全技术社区: https://xianzhi.aliyun.com/forum/user/5274 前面介绍了几种 File 结构体的攻击方式,其中包括修改 vtab ...

  7. Memory map of an object array

    Student类: package com.itheima; /* * 自动生成构造方法: * 代码区域右键 -- Source -- Generate Constructors from Super ...

  8. oracle常见的等待事件说明

    转自 http://blog.itpub.net/29371470/viewspace-1063994/ 1. Buffer busy waits 从本质上讲,这个等待事件的产生仅说明了一个会话在等待 ...

  9. Android组件系列----Activity的生命周期

    [声明] 欢迎转载,但请保留文章原始出处→_→ 生命壹号:http://www.cnblogs.com/smyhvae/ 文章来源:http://www.cnblogs.com/smyhvae/p/3 ...

  10. leetCode题解之反转字符串中的元音字母

    1.问题描述 Reverse Vowels of a String Write a function that takes a string as input and reverse only the ...