sefibk
the settings for 2x downsampling
Hi. Thank you for sharing the code. Could you tell me how to set the following parametres when generating 2x downsampled lr testing images, so make the gererated images are consistent with the dataset you used? Thanks
scale_factor = np.array([4, 4]) # choose scale-factor avg_sf = np.mean(scale_factor) # this is calculated so that min_var and max_var will be more intutitive min_var = 0.175 * avg_sf # variance of the gaussian kernel will be sampled between min_var and max_var max_var = 2.5 * avg_sf k_size = np.array([21, 21]) # size of the kernel, should have room for the gaussian noise_level = 0.4 # this option allows deviation from just a gaussian, by adding multiplicative noise noise
The dataset was generated with some randomness. You can either take what we generated or generate them yourself but it will not be identical.
The dataset was generated with some randomness. You can either take what we generated or generate them yourself but it will not be identical.
Thanks. Could you tell me the values of the following parameters you used to generate 2x/4x testing images?
scale_factor = np.array([4, 4]) # choose scale-factor avg_sf = np.mean(scale_factor) # this is calculated so that min_var and max_var will be more intutitive min_var = 0.175 * avg_sf # variance of the gaussian kernel will be sampled between min_var and max_var max_var = 2.5 * avg_sf k_size = np.array([21, 21]) # size of the kernel, should have room for the gaussian noise_level = 0.4 # this option allows deviation from just a gaussian, by adding multiplicative noise noise
Section 5.2 doesn't specify the scale factors. Did you use the same settings (e.g. kernel width, rotation and noise) for both scale factor 2 and 4?
Yes we generated x2 kernels with those parameters and downscaled the image twice or generated a x4 kernel analytically and downscaled once by 4 (2 identical possible ways)