Comments (1)
https://github.com/tetrachrome/subpixel readme를 참고하자면
PS연산의 수식은 위와 같고
numpy로 구현하면
def PS(I, r):
assert len(I.shape) == 3
assert r>0
r = int(r)
O = np.zeros((I.shape[0]*r, I.shape[1]*r, I.shape[2]/(r*2)))
for x in range(O.shape[0]):
for y in range(O.shape[1]):
for c in range(O.shape[2]):
c += 1
a = np.floor(x/r).astype("int")
b = np.floor(y/r).astype("int")
d = c*r*(y%r) + c*(x%r)
print a, b, d
O[x, y, c-1] = I[a, b, d]
return O
위와 같습니다. 학습할 수 있는 파라미터가 없죠?
PS자체는 Convolution이 없지만
PS Conv layer는 PS입력 크기를 맞추기위해
이전 층 출력 => conv => PS => 출력
을 해서 이름을 PS Conv라고 지은것 같습니다.
from 2019-deeplearning-superresolution.
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from 2019-deeplearning-superresolution.