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

Zero-Reference or Low-Light Image Enhancement

1. Abstract, Introduction, Relative work

  • 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

2. DCE-Net

  • 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


3. Light-Enhancement curves

  • 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
  • image-20210608121856230

  • 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
    • image-20210608121950720
  • Pixel-Wise Curve

    • 각각의 픽셀이 다른 alpha, α 값을 가질 수 있도록 공식 수정
    • image-20210608122438383

4. Non-Reference Loss Functions

4.1. Spatial Consistency Loss

  • Encourages to preserve spatial coherence
  • image-20210608124338020
  • 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


4.2. Exposure Control Loss

  • 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


4.3. Color Constancy Loss

  • “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


4.4. Illumination Smoothness Loss

  • To preserve the monotonicity relations between neighboring pixels
  • 코드
  • image-20210608125701311

5. Results


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