Don't divide test image into patches, don't remove colors and calculate PSNR and SSIM.

ESPCN.py

@@ -12,7 +12,6 @@ class Model(object):
     self.images = config.images
     self.batch = config.batch
     self.label_size = config.label_size
-    self.c_dim = config.c_dim
 
   def model(self):
     d = self.model_params

FSRCNN.py

@@ -15,7 +15,6 @@ class Model(object):
     self.batch = config.batch
     self.image_size = config.image_size - self.padding
     self.label_size = config.label_size
-    self.c_dim = config.c_dim
 
   def model(self):
 

LapSRN.py

@@ -15,7 +15,6 @@ class Model(object):
     self.batch = config.batch
     self.image_size = config.image_size - self.padding
     self.label_size = config.label_size
-    self.c_dim = config.c_dim
 
   def model(self):
     d, m, r = self.model_params

README.md

@@ -3,7 +3,7 @@ TensorFlow implementation of the Fast Super-Resolution Convolutional Neural Netw
 
 ## Prerequisites
  * Python 3
- * TensorFlow-gpu >= 1.3
+ * TensorFlow-gpu >= 1.8
  * CUDA & cuDNN >= 6.0
  * Pillow
  * ImageMagick (optional)

main.py

@@ -12,7 +12,6 @@ flags.DEFINE_boolean("fast", False, "Use the fast model (FSRCNN-s) [False]")
 flags.DEFINE_integer("epoch", 10, "Number of epochs [10]")
 flags.DEFINE_integer("batch_size", 32, "The size of batch images [32]")
 flags.DEFINE_float("learning_rate", 1e-4, "The learning rate of the adam optimizer [1e-4]")
-flags.DEFINE_integer("c_dim", 1, "Dimension of image color [1]")
 flags.DEFINE_integer("scale", 2, "The size of scale factor for preprocessing input image [2]")
 flags.DEFINE_integer("radius", 1, "Max radius of the deconvolution input tensor [1]")
 flags.DEFINE_string("checkpoint_dir", "checkpoint", "Name of checkpoint directory [checkpoint]")

model.py

@@ -26,8 +26,6 @@ class Model(object):
     self.arch = config.arch
     self.fast = config.fast
     self.train = config.train
-    self.c_dim = config.c_dim
-    self.is_grayscale = (self.c_dim == 1)
     self.epoch = config.epoch
     self.scale = config.scale
     self.radius = config.radius
@@ -51,8 +49,12 @@ class Model(object):
 
 
   def init_model(self):
-    self.images = tf.placeholder(tf.float32, [None, self.image_size, self.image_size, self.c_dim], name='images')
-    self.labels = tf.placeholder(tf.float32, [None, self.label_size, self.label_size, self.c_dim], name='labels')
+    if self.train:
+        self.images = tf.placeholder(tf.float32, [None, self.image_size, self.image_size, 1], name='images')
+        self.labels = tf.placeholder(tf.float32, [None, self.label_size, self.label_size, 1], name='labels')
+    else:
+        self.images = tf.placeholder(tf.float32, [None, None, None, 1], name='images')
+        self.labels = tf.placeholder(tf.float32, [None, None, None, 1], name='labels')
     # Batch size differs in training vs testing
     self.batch = tf.placeholder(tf.int32, shape=[], name='batch')
 
@@ -154,20 +156,24 @@ class Model(object):
 
   
   def run_test(self):
-    test_data, test_label, nx, ny = test_input_setup(self)
+    test_data, test_label = test_input_setup(self)
 
     print("Testing...")
 
     start_time = time.time()
-    result = self.pred.eval({self.images: test_data, self.labels: test_label, self.batch: nx * ny})
-    print("Took %.3f seconds" % (time.time() - start_time))
+    result = np.clip(self.pred.eval({self.images: test_data, self.labels: test_label, self.batch: 1}), 0, 1)
+    passed = time.time() - start_time
+    img1 = tf.convert_to_tensor(test_label, dtype=tf.float32)
+    img2 = tf.convert_to_tensor(result, dtype=tf.float32)
+    psnr = self.sess.run(tf.image.psnr(img1, img2, 1))
+    ssim = self.sess.run(tf.image.ssim(img1, img2, 1))
+    print("Took %.3f seconds, PSNR: %.6f, SSIM: %.6f" % (passed, psnr, ssim))
 
-    result = merge(result, [nx, ny, self.c_dim])
-    result = result.squeeze()
+    result = merge(self, result)
     image_path = os.path.join(os.getcwd(), self.output_dir)
     image_path = os.path.join(image_path, "test_image.png")
 
-    array_image_save(result * 255, image_path)
+    array_image_save(result, image_path)
 
   def save(self, step):
     model_name = self.model.name + ".model"

utils.py

@@ -240,45 +240,48 @@ def train_input_setup(config):
   return (arrdata, arrlabel)
 
 def test_input_setup(config):
-  """
-  Read image files, make their sub-images, and save them as a h5 file format.
-  """
   sess = config.sess
-  image_size, label_size, stride, scale, padding = config.image_size, config.label_size, config.stride, config.scale, config.padding // 2
 
   # Load data path
   data = prepare_data(sess, dataset="Test")
 
-  sub_input_sequence, sub_label_sequence = [], []
-
-  pic_index = 2 # Index of image based on lexicographic order in data folder
-  input_, label_ = preprocess(data[pic_index], config.scale)
+  input_, label_ = preprocess(data[2], config.scale)
 
   if len(input_.shape) == 3:
     h, w, _ = input_.shape
   else:
     h, w = input_.shape
 
-  nx, ny = 0, 0
-  for x in range(0, h - image_size + 1, stride):
-    nx += 1
-    ny = 0
-    for y in range(0, w - image_size + 1, stride):
-      ny += 1
-      sub_input = input_[x : x + image_size, y : y + image_size]
-      x_loc, y_loc = x + padding, y + padding
-      sub_label = label_[x_loc * scale : x_loc * scale + label_size, y_loc * scale : y_loc * scale + label_size]
+  arrdata = np.pad(input_.reshape([1, h, w, 1]), ((0,0),(2,2),(2,2),(0,0)), 'reflect')
 
-      sub_input = sub_input.reshape([image_size, image_size, 1])
-      sub_label = sub_label.reshape([label_size, label_size, 1])
+  if len(label_.shape) == 3:
+    h, w, _ = label_.shape
+  else:
+    h, w = label_.shape
 
-      sub_input_sequence.append(sub_input)
-      sub_label_sequence.append(sub_label)
+  arrlabel = label_.reshape([1, h, w, 1])
 
-  arrdata = np.asarray(sub_input_sequence)
-  arrlabel = np.asarray(sub_label_sequence)
+  return (arrdata, arrlabel)
 
-  return (arrdata, arrlabel, nx, ny)
+def merge(config, Y):
+  """
+  Merges super-resolved image with chroma components
+  """
+  h, w = Y.shape[1], Y.shape[2]
+  Y = Y.reshape(h, w, 1) * 255
+  Y = Y.round().astype(np.uint8)
+
+  data = prepare_data(config.sess, dataset="Test")
+  src = Image.open(data[2]).convert('YCbCr')
+  (width, height) = src.size
+  if downsample is False:
+    src = src.resize((width * config.scale, height * config.scale), Image.BICUBIC)
+    (width, height) = src.size
+  CbCr = np.frombuffer(src.tobytes(), dtype=np.uint8).reshape(height, width, 3)[:,:,1:]
+
+  img = np.concatenate((Y, CbCr), axis=-1)
+
+  return img
 
 def save_params(sess, params):
   param_dir = "params/"
@@ -313,24 +316,11 @@ def save_params(sess, params):
 
   h.close()
 
-def merge(images, size):
-  """
-  Merges sub-images back into original image size
-  """
-  h, w = images.shape[1], images.shape[2]
-  img = np.zeros((h * size[0], w * size[1], size[2]))
-  for idx, image in enumerate(images):
-    i = idx % size[1]
-    j = idx // size[1]
-    img[j*h:j*h+h, i*w:i*w+w, :] = image
-
-  return img
-
 def array_image_save(array, image_path):
   """
   Converts np array to image and saves it
   """
-  image = Image.fromarray(array)
+  image = Image.fromarray(array, 'YCbCr')
   if image.mode != 'RGB':
     image = image.convert('RGB')
   image.save(image_path)