The result is not satisfactory because PSNR reaches about 23? What Happened? about deepupe HOT 3 OPEN

Additional, I use cv2.PSNR.

from deepupe.

Is the testing data from our link? At first, you should use adobe5k default option to process the data, and try use our matlab psnr code and SSIM code from Wang zhou's homepage.

I have managed to run the code and I test on the same test datasets, only to find disappointing result. Is there anyone encoutering the same problem.
Listed is my results:
共 500 张测试图片
平均结果 :
PSNR : 12.240499250275413 ====> 22.88365191146186
SSIM : 0.6161701658815372 ====> 0.7980569710290214
test images is the same( Adobe FiveK 4500-5000 )
My config is also:
python 2.7
tensorflow-gpu=1.1.0
cuda8.0

from deepupe.

I changed my code to compute psnr.

In OpenCV:
  PSNR = 10 * log10( range / mse of all 3 channels )
In your codes:
  PSNR = Mean( 10 * log10( range / mse of one channel) for 3 channels)

The result is so amazing:

共 500 张图像
1 a4844-kme_0393.jpg    25.909085943496024    29.921717892553414
2 a4726-DSC_0120.jpg    22.63510845360183    27.363339705226107
......
500 a4891-DSC_0069.jpg    24.064769515852085    29.045309176387974
opencv_psnr : 22.88365191146186
mean_psnr : 28.895108206611532

The difference between different calculations is so great！
Which is more authoritative?

Test images I use may be different with yours on resolution, therefore, 30 is possible

My codes is listed below:

import os
import cv2
import numpy

# generated images dir
dir_name = './DeepUPE/23/'
names = os.listdir(dir_name)
print('共  ', len(names), ' 张图像')

def compute_mean(a, b):
	mse = numpy.square(a - b)
	mse_3 = [mse[:, :, i].mean() for i in range(3)]
	psnr_3 = [10 * numpy.log10(255 * 255 / it) for it in mse_3]
	return numpy.mean(psnr_3)

opencv_psnr = 0
mean_psnr = 0
for i, it in enumerate(names):
	# l = cv2.imread('./bad/' + it)
	r = cv2.imread('./normal/' + it)
	e = cv2.imread(dir_name + it)
	a = cv2.PSNR(r, e)
	opencv_psnr += a
	b = compute_mean(r, e)
	mean_psnr += b
	print(i + 1, it, a, b)
opencv_psnr /= (i + 1)
mean_psnr /= (i + 1)
print('opencv_psnr  :  ', opencv_psnr)
print('mean_psnr  :  ', mean_psnr)

from deepupe.