## 3 Methodology  
### Analysis of the HDR Image Reconstruction Problem  
Some methods decompose the HDR image reconstruction process into multiple subtasks. For instance, ref. [23] broke the process into denoising and quantization, while ref. [43] split it into denoising, exposure adjustment, and tone extension. According to the imaging formula, ref. [44] separated the process into recovering overexposed areas, adjusting imaging parameters, and denoising. Ref. [58] distinguished global and local operations in the reconstruction task. Few existing methods rely on the imaging pipeline for HDR image reconstruction, which primarily focuses on a portion of the scene during the generation of HDR images and fails to address both the issue of detail recovery and the dequantization of overexposed regions. Although ref. [36] was based on the imaging pipeline, it did not delve into the LDR imaging pipeline, instead merely outlining the three modeling steps. In contrast, we describe in detail the complete pipeline process from light capture to LDR image based on the imaging pipeline principle, along with the introduced operations that lead to loss of image quality. The principle of the LDR imaging pipeline [40; 59] is illustrated in Figure 2:  
1. The lens focuses the HDR Scene Luminance and forms an image on the sensor. The aperture controls the amount of light entering the lens, while the shutter speed determines the duration of light exposure to the sensor. Together, they determine the exposure value (EV) of the image. The relationship between these factors is given in the following formula: \(EV=log_{2}\frac{N^{2}}{t}\), where \(N\) represents the aperture value of the lens and \(t\) represents the exposure time in seconds. 2. The sensor outputs a voltage corresponding to the amount of light, and camera noise is introduced during the process of converting the optical signal into the electrical signal, as shown in the following formula: \(I_{e}=G\cdot I_{o}+N\), where \(I_{e}\) denotes the converted electrical signal, \(I_{o}\) represents the optical signal, \(G\) represents the sensor gain, and \(N\) represents noise. Additionally, excessive electrical signals in the conversion process are cropped due to saturation, leading to the loss of information in the overexposed area, which can be expressed as: \(I_{output}=\min(I_{e},I_{max})\), where \(I_{output}\) represents the sensor's electrical signal output and \(I_{max}\) represents the maximum threshold for electrical signals. 3. The sensor's electrical signal is converted into digital format by an Analog-to-Digital (AD) converter. This process involves quantizing small voltage values, which can lead to a loss of tone in the underexposed areas and artifacts caused by errors in smoothing the gradients. It is important to note that enhancing low-light conditions [60] and recovering tone and content in underexposed areas are two distinct tasks. Specifically, the quantization formula can be expressed as: \(I_{q}=\left\lfloor\frac{b}{255}\cdot I_{output}+\frac{05}{255}\right\rfloor\). In this formula, \(I_{q}\) is the quantizing pixel value, typically an 8-bit integer, and \(b\) is a scaling factor, usually an integer between 0 and 255. 4. After the AD conversion, the resulting image is known as a RAW Image Format (RAW) image, which encompasses the raw lighting data captured from the camera sensor. To accurately represent the way humans perceive the scene, the Camera Response Function (CRF) is necessary for the nonlinearization process. Subsequent tone mapping of the image further compresses the luminance range of the image through the nonlinearization operation, enabling the image to adapt to the dynamic range of the display device. The introduced nonlinearization operation can be integrally represented as: \(I_{n}=f(I_{q})\), where \(f(\cdot)\) is the nonlinearization operation. 5. To facilitate distribution and downloading over the internet, the processed image is compressed into JPEG format, which can potentially introduce compression artifacts.