计算机视觉应用开发课件：基于SRGAN的图像超分辨率重建

基于SRGAN的图像超分辨率重建案例描述01任务案例目标02任务案例分析03任务案例实施04任务1案例描述1案例描述本案例基于SRGAN，并在T91数据集上训练，实现图像的超分辨重建。2案例目标案例目标学会搭建SRGAN超分辨率重建网络；

通过约束对抗损失和内容损失训练SRGAN 网络以实现图像的超分辨率重建。23案例分析案例分析SRGAN网络同样需要一些低分辨率图像和原始高分辨率图像，低分辨率图像用做输入数据来训练SRGAN网络模型，高分辨率图片用来检测超分图像重建的效果。为此除了原始图像，需要手动构造低分辨率图像。整个案例的流程如下：（1）加载数据集，进行规范化；（2）构造低分辨率图像；（3）搭建SRGAN网络；（4）编译并训练；（5）预测并对比。34案例实施4案例实施 1.数据预处理创建utils.py，首先是一些预处理文件，包括读取，存储图片等函数。#读入图片，并对图片做尺寸裁减，并将所有图片进行归一化处理img=img[start_h:start_h+fine_size,start_w:start_w+fine_size,:]img=(img-127.5)/127.5#保存图片以及图片名称defsave_images(images,size,filename):returnscipy.misc.imsave(filename,np.squeeze(images))4案例实施2.定义网络层创建layer.py，主要定义一些特定的网络模块以及所需的损失函数。其中导入tensorflow.contrib.slim库要求numpy版本为1.20.0以下，scipy版本为1.2.0。#定义残差网络块defres_block(input_x,out_channels=64,k=3,s=1,scope='res_block'):withtf.variable_scope(scope):x=input_xinput_x=slim.conv2d_transpose(input_x,out_channels,k,s)input_x=slim.batch_norm(input_x,scope='bn1')input_x=tf.nn.relu(input_x)input_x=slim.conv2d_transpose(input_x,out_channels,k,s)input_x=slim.batch_norm(input_x,scope='bn2')returnx+input_x4案例实施#定义PSNR损失函数defPSNR(real,fake):mse=tf.reduce_mean(tf.square(127.5*(real-fake)+127.5),axis=(-3,-2,-1))psnr=tf.reduce_mean(10*(tf.log(255*255/tf.sqrt(mse))/np.log(10)))returnpsnr3.训练模型

创建SRGAN.py，包含SRGAN模型的构建以及训练测试过程。

定义生成器。生成器包含五个卷积模块和五个残差块和两个像素随机操作。4案例实施conv1=tf.nn.relu(slim.conv2d_transpose(input_x,64,3,1,scope='g_conv1'))shortcut=conv1#res_block(input_x,out_channels=64,k=3,s=1,scope='res_block'):res1=res_block(conv1,64,3,1,scope='g_res1')res2=res_block(res1,64,3,1,scope='g_res2')res3=res_block(res2,64,3,1,scope='g_res3')res4=res_block(res3,64,3,1,scope='g_res4')res5=res_block(res4,64,3,1,scope='g_res5')conv2=slim.batch_norm(slim.conv2d_transpose(res5,64,3,1,scope='g_conv2'),scope='g_bn_conv2')conv2_out=shortcut+conv2#pixel_shuffle_layer(x,r,n_split):conv3=slim.conv2d_transpose(conv2_out,256,3,1,scope='g_conv3')shuffle1=tf.nn.relu(pixel_shuffle_layer(conv3,2,64))#64*2*2conv4=slim.conv2d_transpose(shuffle1,256,3,1,scope='g_conv4')shuffle2=tf.nn.relu(pixel_shuffle_layer(conv4,2,64))conv5=slim.conv2d_transpose(shuffle2,3,3,1,scope='g_conv5')self.g_vars=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,'generator')4案例实施定义鉴别器。鉴别器包含四个卷积模块和两个全连接层。conv1=leaky_relu(slim.conv2d(input_x,64,3,1,scope='d_conv1'))conv1_1=leaky_relu(slim.batch_norm(slim.conv2d(conv1,64,3,2,scope='d_conv1_1'),scope='d_bn_conv1_1'))conv2=leaky_relu(slim.batch_norm(slim.conv2d(conv1_1,128,3,1,scope='d_conv2'),scope='d_bn_conv2'))conv2_1=leaky_relu(slim.batch_norm(slim.conv2d(conv2,128,3,2,scope='d_conv2_1'),scope='d_bn_conv2_1'))conv3=leaky_relu(slim.batch_norm(slim.conv2d(conv2_1,256,3,1,scope='d_conv3'),scope='d_bn_conv3'))conv3_1=leaky_relu(slim.batch_norm(slim.conv2d(conv3,256,3,2,scope='d_conv3_1'),scope='d_bn_conv3_1'))conv4=leaky_relu(slim.batch_norm(slim.conv2d(conv3_1,512,3,1,scope='d_conv4'),scope='d_bn_conv4'))conv4_1=leaky_relu(slim.batch_norm(slim.conv2d(conv4,512,3,2,scope='d_conv4_1'),scope='d_bn_conv4_1'))

conv_flat=tf.reshape(conv4_1,[self.batch_size,-1])dense1=leaky_relu(slim.fully_connected(conv_flat,1024,scope='d_dense1'))dense2=slim.fully_connected(dense1,1,scope='d_dense2')4案例实施定义优化器，分为两个优化器，分别优化生成器和鉴别器self.d_optim=tf.train.AdamOptimizer(learning_rate=self.learning_rate,beta1=self.beta1,beta2=self.beta2).minimize(self.d_loss,var_list=self.d_vars)self.g_optim=tf.train.AdamOptimizer(learning_rate=self.learning_rate,beta1=self.beta1,beta2=self.beta2).minimize(self.g_loss,var_list=self.g_vars)定义内容损失和对抗损失：content_loss=inference_mse_content_loss(real,fake)d