《Learning a Deep Convolutional Network for Image Super-Resolution》的学习。

此图展示了SRCNN的网络结构。

此图展示了在卷积神经网络下稀疏编码的结构。

SRCNN整个算法包括三个操作：

    1. Patch extraction and representation

// 主函数
from model import SRCNN
from utils import input_setup   import numpy as np
import tensorflow as tf import pprint
import os   flags = tf.app.flags
flags.DEFINE_integer("epoch", 15000, "Number of epoch [15000]")
flags.DEFINE_integer("batch_size", 128, "The size of batch images [128]")
flags.DEFINE_integer("image_size", 33, "The size of image to use [33]")
flags.DEFINE_integer("label_size", 21, "The size of label to produce [21]")
flags.DEFINE_float("learning_rate", 1e-4, "The learning rate of gradient descent algorithm [1e-4]")
flags.DEFINE_integer("c_dim", 1, "Dimension of image color. [1]")
flags.DEFINE_integer("scale", 3, "The size of scale factor for preprocessing input image [3]")
flags.DEFINE_integer("stride", 14, "The size of stride to apply input image [14]")
flags.DEFINE_string("checkpoint_dir", "checkpoint", "Name of checkpoint directory [checkpoint]")
flags.DEFINE_string("sample_dir", "sample", "Name of sample directory [sample]")
flags.DEFINE_boolean("is_train", True, "True for training, False for testing [True]")
FLAGS = flags.FLAGS    pp = pprint.PrettyPrinter()    def main(_):    pp.pprint(flags.FLAGS.__flags)  if not os.path.exists(FLAGS.checkpoint_dir):    os.makedirs(FLAGS.checkpoint_dir)   if not os.path.exists(FLAGS.sample_dir):    os.makedirs(FLAGS.sample_dir)   with tf.Session() as sess:  srcnn = SRCNN(sess,    image_size=FLAGS.image_size,   label_size=FLAGS.label_size,   batch_size=FLAGS.batch_size,   c_dim=FLAGS.c_dim,     checkpoint_dir=FLAGS.checkpoint_dir,   sample_dir=FLAGS.sample_dir)   srcnn.train(FLAGS)  if __name__ == '__main__':  tf.app.run()

1. 利用Relu作为激活函数虽然速度快，但是训练的时候很”脆弱”，很容易就”die”；
2. SRCNN需要先通过双三次插值的方法对低分辨率的图片插值放大尺寸。

[1] https://zhuanlan.zhihu.com/p/49846783

[2] https://medium.com/coinmonks/review-srcnn-super-resolution-3cb3a4f67a7c

[3] http://jiaqianlee.com/2018/06/09/SRCNN/