# Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
###### Abstract  
Over the last decade, supervised denoising models, trained on extensive datasets, have exhibited remarkable performance in image denoising, owing to their superior denoising effects. However, these models exhibit limited flexibility and manifest varying degrees of degradation in noise reduction capability when applied in practical scenarios, particularly when the noise distribution of a given noisy image deviates from that of the training images. To tackle this problem, we put forward a two-stage denoising model that is actualized by attaching an unsupervised fine-tuning phase after a supervised denoising model processes the input noisy image and secures a denoised image (regarded as a preprocessed image). More specifically, in the first stage we replace the convolution block adopted by the U-shaped network framework (utilized in the deep image prior method) with the Transformer module, and the resultant model is referred to as a U-Transformer. The U-Transformer model is trained to preprocess the input noisy images using noisy images and their labels. As for the second stage, we condense the supervised U-Transformer model into a simplified version, incorporating only one Transformer module with fewer parameters. Additionally, we shift its training mode to unsupervised training, following a similar approach as employed in the deep image prior method. This stage aims to further eliminate minor residual noise and artifacts present in the preprocessed image, resulting in clearer and more realistic output images. Experimental results illustrate that the proposed method achieves significant noise reduction in both synthetic and real images, surpassing state-of-the-art methods. This superiority stems from the supervised model's ability to rapidly process given noisy images, while the unsupervised model leverages its flexibility to generate a fine-tuned network, enhancing noise reduction capability. Moreover, with support from the supervised model providing higher-quality preprocessed images, the proposed unsupervised fine-tuning model requires fewer parameters, facilitating rapid training and convergence, resulting in overall high execution efficiency. s 2024 14  
address the issue of image denoising. Until very recently, it has continued to be extensively employed. As an example, in [14] [PERSON] and [PERSON] introduced an innovative expectation maximization-based framework for concurrent stripe estimation and image denoising. Within this framework, they derived a nonparametric estimator to precisely compute the conditional expectation of the true image. They opted to utilize a modified non-local means (NLM) [11] algorithm for this purpose. While these traditional methods achieved satisfactory results in denoising at the time, the inherent complexity of the denoising problem led to intricate algorithm design and long execution times. Over the past decade, the rapid advancement of deep neural networks (DNNs) in image processing and analysis has brought about significant breakthroughs in image denoising tasks, leveraging their remarkable learning capabilities and nonlinear mapping abilities [15; 16]. As one of the early endeavors in this field, [PERSON] and [PERSON] et al. applied a simple convolutional neural networks(CNN) for image denoising, which demonstrated comparable performance to wavelet and Markov random field (MRF) methods [17]. Subsequently, researchers have proposed a series of more advanced deep-learning methods, including TNRD [18], DnCNN [19], FFDNet [20], GANID [21], and CBDNet [22], which have exhibited excellent denoising performance compared to traditional non-deep-learning methods. [PERSON] et al. [19] incorporated deep architecture, residual-learning mechanisms [23], and batch normalization processing [24] into image denoising with DnCNN. This approach incorporates convolution layers, rectified linear units [25], and BN blocks. The initial estimation of residual noise within the input noisy image enables subtraction from the original noisy image, resulting in a clean output.