# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
###### Abstract
While these methods can effectively enhance the overall brightness and contrast of low-light images, they are computationally intensive due to the complex decomposition and reconstruction processes. Additionally, the parameter tuning required for optimal performance is often non-trivial, making it challenging to adapt these methods to diverse low-light scenarios without extensive manual intervention [20]. Gamma correction is a classical nonlinear transformation method that is computationally simple and efficient. Its nonlinear characteristics align well with the human visual system's perception of brightness [21], making it suitable for enhancing the overall luminance of low-light images. However, most Gamma correction methods typically employ a fixed Gamma value for the entire image, which cannot adequately adapt to the varying brightness levels within an image. This limitation often results in overexposure in brighter regions or insufficient brightness adjustment in darker areas, thereby failing to achieve balanced enhancement across the entire image. Other traditional approaches, such as those based on wavelet transforms [22; 23], fuzzy set theory [24; 25], and multi-scale fusion [26], have also been explored for low-light image enhancement. The wavelet transform-based methods excel at effectively separating and processing image information at different scales, enhancing details while suppressing noise, with good edge preservation. For example, the method proposed by [PERSON] et al. [22] in 2017 achieved effective contrast enhancement and noise reduction. However, these methods are often sensitive to the choice in thresholds and basis functions, may lead to artifacts or color distortion if mishandled, and are relatively computationally complex. The fuzzy set theory-based methods can naturally model the fuzziness and uncertainty of image brightness, offering flexible nonlinear mapping rules and strong adaptability. Yet, their performance heavily relies on the design of membership functions and fuzzy rules, and may introduce unnatural appearance or noise if not applied carefully. The multi-scale fusion-based methods can integrate complementary information from different scales or processing results, leveraging the strengths of various techniques to typically achieve more robust and natural enhancement results. For instance, the multi-scale fusion approach proposed by [PERSON] et al. [26] in 2023 yielded promising results. Nevertheless, these methods are generally computationally intensive, requiring the generation and processing of multiple inputs, the design of fusion weights is crucial and challenging, and improper fusion can easily introduce artifacts or blurring. In summary, while the traditional methods have laid the foundation for low-light image enhancement, their inherent limitations in terms of computational complexity, parameter tuning, and adaptability to diverse scenarios necessitate the development of more advanced and flexible techniques to achieve high-quality enhancement results in complex low-light environments. In recent years, the field of image enhancement has seen a significant increase in the development of low-illumination enhancement methods based on deep learning. These methods have not only achieved superior performance compared to traditional approaches but also demonstrated promising prospects for further development. The advent of deep learning has fundamentally transformed the landscape of low-light image enhancement by leveraging the powerful representation learning capabilities of neural networks. The pioneering work in this domain can be traced back to 2017, when [PERSON] et al. [27] proposed a deep autoencoder approach for natural low-light image enhancement, known as LLNet, marking the beginning of a new era in low-light enhancement.