## 1 Introduction  
Recently, mobile digital imaging devices such as digital cameras, smartphones, and scanners have gained widespread popularity. Social networking platforms transmit and generate countless digital images and videos daily. In addition, cyberspace is being inundated with a substantial volume of generative artificial intelligence content exemplified by Sora v2. The digital image not only assumes a pivotal role in daily life and professional settings for information storage and communication, but also exerts an increasingly significant influence across various domains including social networks, news dissemination, business operations, political landscapes, military affairs, and artistic endeavors, among others. Simultaneously, the inherent characteristics of digital images and videos, such as their susceptibility to forgery and tampering, limited traceability, and challenging evidence collection, present significant obstacles for electronic data forensics and judicial identification. Recently, digital image tracing has emerged as a prominent subject of academic investigation. Notably, forensic technology based on PRNU fingerprints [1; 2; 3; 4; 5; 6; 7; 8] has gained significant attention among researchers. During the imaging process, the PRNU noise arises from the inherent non-uniformity of silicon in the digital imaging device's image sensors and exhibits distinctive characteristics of uniqueness and stability. Consequently, it can be employed as a \"fingerprint\" for source camera attribution. Through an analysis of the extracted device fingerprints from digital images, investigators successfully assigned the origin of a specific image to its source camera. This not only aids in the discovery of clues and the clarification of investigative directions for case resolution, but also serves as vital supplementary evidence in judicial practice, thereby enhancing the establishment of a comprehensive chain of evidence. The extraction of a PRNU fingerprint has been extensively pursued to achieve the highest possible purity. Initially, [PERSON] et al. [1] proposed a PRNU extraction algorithm based on the wavelet filtering denoising method. In this approach, an initial wavelet decomposition is performed on the digital image using Daubechies filters to estimate the local image variance. Subsequently, a local Wiener filtering technique is applied in the wavelet domain to obtain a clean noise-free image. Finally, the noise residuals of multiple images are jointly augmented to derive the PRNU fingerprint. This groundbreaking research laid the groundwork for the investigation of imaging device fingerprints and marked the initial application of PRNU fingerprints in image forensics. The denoising method proposed by [PERSON] et al. [2] is based on a combination of three-dimensional block matching, transformation algorithms, and spatial algorithms. The proposed method initially partitions the digital image into blocks of equal size and applies a transformation. Subsequently, a matching algorithm is employed to merge similar patches into a 3D matrix, which undergoes further transformation to separate signal from noise. The noise components are then eliminated through either hard or soft threshold filtering. Finally, a stereo inverse transform is conducted to reassemble the image blocks into an integrated image. [PERSON] et al. [3] conducted an experiment using a wavelet-based PRNU extraction algorithm on a dataset comprising over one million digital images sourced from 6896 cameras of 150 different models, which were obtained from online image sharing websites. The aforementioned studies primarily focus on the removal of Gaussian noise and achieve satisfactory outcomes. However, real-world images exhibit not only additive noise such as Gaussian noise but also non-additive noises including Poisson noise, salt-and-pepper noise, gamma noise, and Rayleigh noise. These noises exhibit a higher degree of contamination in the image and pose greater challenges for removal.