# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
## 2 Related Works  
The Zero-DCE [33] method, introduced by [PERSON] et al. in 2020, represents a zero-reference approach for enhancing low-light images. Drawing inspiration from the curve adjustment techniques used in photo editing software to modify image brightness, the authors designed a specific curve for low-light image enhancement, referred to as the LE curve. This innovation effectively transforms the low-light-enhancement task into an image-specific curve estimation problem. The mathematical formulation of the LE curve is given by  
\[LE(I(\mathbf{x});\alpha)=I(\mathbf{x})+\alpha I(\mathbf{x})(1-I(\mathbf{x})), \tag{1}\]  
where \(I(\mathbf{x})\) denotes the normalized pixel value at position \(\mathbf{x}\), constrained within the range [0, 1], and \(\alpha\) is a learnable parameter that governs the curvature of the enhancement curve. This pixel-wise adjustment mechanism facilitates spatially varying enhancement, allowing the method to adapt dynamically to different regions within the image.  
The network architecture of Zero-DCE, known as DCE-Net, is composed of seven layers, with skip connections incorporated to further preserve image details. This structure is both concise and efficient, utilizing 3 \(\times\) 3 convolutional kernels and ReLU activation functions in each layer, while the final layer employs a Tanh activation function, ensuring a lightweight design. The output layer generates 24 channels, which are then reshaped into 8 three-channel maps. These maps serve as adjustment parameters for the RGB channels in multiple iterations of the curve mapping function. By iteratively applying the LE curve, the model can gradually optimize the brightness and contrast of the image through progressive refinements. Moreover, Zero-DCE employs a set of carefully designed loss functions to guide the training process, eliminating the need for paired images. These loss functions include spatial consistency loss, exposure control loss, illumination smoothness loss, and color constancy loss. These non-reference losses work in conjunction with the iterative curve mapping process to generate pixel-specific adjustment parameters during training, enabling the model to adaptively enhance each pixel based on its surrounding context and content characteristics. Unlike other supervised learning methods that require a large amount of paired images, the zero-reference learning strategy of Zero-DCE significantly reduces the dependence on training data. This approach not only achieves satisfactory visual quality but also demonstrates clear advantages in computational efficiency and practicality, offering a solution for low-light image enhancement that balances effectiveness and efficiency.  
To further lighten the model and accelerate computational speed, [PERSON] et al. [34] reintroduced Zero-DCE++, an enhanced version of Zero-DCE. One of the primary improvements is the adoption of depthwise-separable convolutions instead of standard convolutions, which significantly reduces the number of network parameters. Additionally, the number of output channels is decreased from twenty-four to three. Unlike Zero-DCE, which requires 24 different parameter mappings over eight iterations, Zero-DCE++ only needs to reuse three-channel (R, G, and B) maps in each iteration. Furthermore, Zero-DCE++ incorporates downsampling to achieve a better balance between enhancement performance and computational cost.