applied sciences  
Boosting the Performance of LLIE Methods via Unsupervised Weight Map Generation Network  
[PERSON] 1, [PERSON] 2,* (c), [PERSON] 2, [PERSON] 2, [PERSON] 2 and [PERSON] 2(c)  
Footnote 11: foot  
conditions such as low light, backlighting, or nighttime settings, which often result in issues like loss of detail, inadequate contrast, and color inconsistencies, thus impeding the extraction of crucial visual features. Addressing this fundamental challenge has been a longstanding issue in the field of computer vision. To uncover hidden details within low-light images and mitigate performance degradation in subsequent visual tasks, significant research efforts have been directed towards the development of improved LLIE techniques over the past decade. In the domain of LLIE, initial methodologies predominantly lean on traditional image processing techniques, such as histogram equalization (HE) [12] and the Retinex theory [13]. HE redistributes luminance values across the histogram to enhance contrast. However, these approaches often prioritize contrast enhancement over addressing the underlying causes of illumination variations, which can lead to the risks of over-enhancement or under-enhancement. Conversely, the Retinex model decomposes images into reflectance and illumination components, with the aim of alleviating lighting variations while preserving reflectance. Mathematically, this model can be represented as:  
\[l=R\cdot L, \tag{1}\]  
where \(R\), \(L\), and \(\cdot\) denote reflectance, illumination, and element-wise multiplication, respectively. Despite the favorable results achieved by Retinex-based methods in improving image contrast, this approach can sometimes lead to significant color cast problems and inadequate enhancement of details in darker areas. These methods generally rely on hand-crafted features and utilize optimization strategies to improve image quality, heavily relying on the accuracy of these pre-defined assumptions. As a result, their ability to capture intricate data patterns, especially in complex lighting scenarios, is limited, which in turn restricts their adaptability and generalization capabilities. To address these limitations, [PERSON] et al. introduced two variants of Retinex: Single-scale Retinex (SSR) [14] and multi-scale Retinex (MSR) [15]. SSR initially employs a Gaussian filter to smooth the illumination map, while MSR extends SSR by incorporating multi-scale Gaussian filters and color restoration. However, both approaches often produce unnatural-looking images and suffer from over-enhancement due to their treatment of reflectance as the final outcome of enhancement. Moreover, [PERSON] proposed LIME [16] as an improvement and extension of Retinex theory. LIME refines the initial illumination estimation based on the Max-RGB assumption and incorporates a structure-preserving constraint. This refinement effectively enhances low-light images while improving brightness and detail. However, there is still room for improvement in terms of the method's complexity and computational efficiency. Furthermore, existing methods exhibit limited efficacy in color enhancement due to the non-linearity across color channels and the complexity of the data. This limitation becomes particularly noticeable in the presence of local color distortions frequently observed in enhanced images. Recently, deep learning has been widely applied in the field of LLIE and has made remarkable progress due to its ability to perform non-linear mapping, feature learning, and representation. In the early stages, [PERSON] et al. developed the low-light net (LLNet) [17], which utilized a stacked-sparse denoising auto-encoder to simultaneously enhance low-light images and reduce noise. However, this approach does not fully consider the characteristics of low-light images, resulting in residual noise and over-smoothing issues in the processed images. To address these limitations, [PERSON] et al. proposed the low-light convolutional neural network (LLCNN) [18].