【IE】 Zero-Reference or Low-Light Image Enhancement

Zero-Reference or Low-Light Image Enhancement

Zero-Reference: Paired or Unpaired Dataset이 필요 없음
Deep Curve Estimation: Pixel value Function을 정의하는 함수의 파라미터를 예측.
non-reference loss functions: 4가지 종류의 Loss로 이뤄져있으며, Self-Supervision이다.
추가적 장점, the potential benefits to face detection in the dark.
전통 기법
1. Retinex theory [13]: reflectance and illumination 부분을 추정하는 이론.
2. uan and Sun [36]: 주어진 이미지에서 ` global optimization algorithm` S-shaped curve를 추정하고 그대로 이미지에 적용
딥러닝 사용 기법
1. CNN based
  - Pared data 필요
  - Wang et al. [28, 2019 CVPR]: estimating the illumination map. paired data that were retouched by three experts.
2. GAN based
  - Unpared data 사용
  - EnlightenGAN [12, 2019 CVPR]: unpaired low/normal light data와 GAN을 사용해서 low-light Image Enhancement. 그러나 careful selection of unpaired training data이 필요하다는 문제점 있음
지금까지의 다른 기법들 문제점
1. Fail to cope with the extreme back light region
2. Generate color artifacts

Input: Image (256×256×3)
Output: a set of pixel-wise curve parameter maps for corresponding higher- order curves
a plain CNN of seven convolutional layers with 32 convolutional kernels of size 3×3.
ReLU activation function.
down-sampling 그리고 batch normalization layers 는 없다.
Last: Tanh activation function, 24 channels (8 iterations (n = 8) x RGB(3) )
RGB 따로 추정하여 얻는 장점
1. Better preserve the inherent color
2. Reduce the risk of over-saturation

Estimate a set of best-fitting Light-Enhancement curves by alpha, α [-1, 1] 사이의 값
Curve 조건
1. each pixel value in the normalized range of [0,1]
2. this curve should be monotonous. (단순 증가 함수, 단순 감소 함수)
3. simple and differentiable
LE-curve의 장점
1. E-curve enables us to increase or decrease the dynamic range of an input image
2. Not only enhancing low-light regions But also removing over-exposure artifacts.
Higher-Order Curve
- The LE-curve defined in Eq. (1) can be applied iteratively.
- Global adjustment since α is used for all pixels. But a global mapping tends to over-/under- enhance local regions.
- n = 1~8
Pixel-Wise Curve
- 각각의 픽셀이 다른 alpha, α 값을 가질 수 있도록 공식 수정

Encourages to preserve spatial coherence
K is the number of local region(이미지 4x4 Poolling) / Ω(i) is the four neighboring regions (top, down, left, right)
Y and I as the average intensity value of the local region in the enhanced version and input image
코드

the well-exposedness level E. We follow existing practices [23,24]. We set E to 0.6
M represents the number of nonoverlapping local re- gions of size 16×16, Y is the average intensity value
코드

“Color in each sensor channel averages to gray over the entire image” 이라는 가정 이용 [논문참조, 2]
Encourage to correct the potential color deviations in the enhanced image.
아래 수식의 J_p denotes the average intensity value of p channel
코드