WolframRhodium

import vapoursynth as vs
from vapoursynth import core as _vscore

class _Plugin:
    def __init__(self, namespace):
        self.__dict__.update((name, getattr(namespace, name)) for name in dir(namespace)) # func_name : func

class _Core:
    def __init__(self):
        self.__dict__.update((name, get_plugin(name)) for name in dir(_vscore)) # (namespace : (func_name : func)) or (attr_name : attr)

WolframRhodium

Official implementation based on Chainer (requires CUDA)

from vapoursynth import core
import chainer
# chainer.global_config.cudnn_deterministic = False

from vs_wadiqam_chainer import wadiqam_fr, wadiqam_nr


model_folder_path = "deepIQA-master\models" # path to the folder that contains model's parameter files

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fmtconv	equivalent	remarks
impulse=[1, 1, 1], fv=-1, fh=-1	std.Convolution([1]*9) or rgvs.RemoveGrain(20)	Radius=1 box filtering. fv and fh are required to force the processing.
scale=1/2, kernel='box'	scale=1/2, kernel='impulse', impulse=[1]*3, kovrspl=3	Box downscaling with a factor of 2
scale=1/4, kernel='box'	scale=1/4, kernel='impulse', impulse=[1]*5, kovrspl=5	Box downscaling with a factor of 4
scale=1/2, kernel='bilinear'	scale=1/2, impulse=[0.5, 1, 0.5], kovrspl=2	Bilinear downscaling with a factor of 2
scale=1/3, kernel='bilinear'	scale=1/3, impulse=[0.5, 1, 0.5], kovrspl=2	Bilinear downscaling with a factor of 3
scale=1/4, kernel='bilinear'	scale=1/4, impulse=[0.5, 1, 0.5], kovrspl=2	Bilinear downscaling with a factor of 4

WolframRhodium

Environment:

Intel Core i7-6700HQ @ 2.60GHz, 8 GB DDR4-2133 x2,

Windows 10 1809, VapourSynth R44 64bit, fmtconv r20(CRC: 6EEDD665)

Result:

Input dimensions	Output dimensions	Format	Backend	FPS

WolframRhodium

{
  "nodes": [
    {
      "op": "null", 
      "name": "data", 
      "inputs": []
    }, 
    {
      "op": "_plus_scalar", 
      "name": "_plusscalar0", 

WolframRhodium

Muonium

章节

1. KNLMeansCL 参数介绍
2. 从Sobel算子到ringmask2()
3. iterLineDarken —— 一种新型迭代式线条加深方法
4. 两种新式图像结构迁移方法
5. MinBlurMod —— 一种新式去点状晕轮方法

WolframRhodium

import numpy as np
import scipy as sp
from scipy import misc
import sympy as sym
import math

# Helper functions

# https://en.wikipedia.org/wiki/Norm_(mathematics)
def calculate_error(diff, norm=0):

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# Parameters
input = YUV420P16
# matrix = "709"
matrix = mvf.GetMatrix(input, matrix, True)

process_y = True
sigma_y = 2.0
basic_y_args = dict(profile="fast", group_size=8, bm_range=6)

process_uv = False