# Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy
## 4 Experimental Results  
### Experimental Environment and Datasets  
We assessed the efficacy and benefits of our suggested two-stage image denoising model by conducting performance comparisons with various state-of-the-art methods, including BM3D [12], DnCNN [19], FFDNet [20], DIP [39], IRCNN [53], DAGL [54], DeamNet [55], DRUNet [56], and SwinIR [29]. Among these methods, BM3D belongs to the category of filter-based denoising methods. It involves grouping similar blocks in noisy images and then recovering their common structure through spatial domain averaging or transform domain filtering, representing an early classic model. DnCNN and FFDNet, proposed by [PERSON] et al., mark a significant milestone by introducing a deep-learning-based framework for image denoising. The unsupervised method DIP demonstrates that appropriate hand-crafted network architectures correspond to better hand-crafted priors. We also include several state-of-the-art supervised methods. The IRCNN can train a set of fast and efficient CNN image denoisers by using flexible model-based optimization methods and fast discriminative learning methods, the DAGL is a state-of-the-art hybrid method, the DeamNet implements an innovative end-to-end trainable and interpretable deep denoising network, the DRUNet achieves plug-and-play image restoration tasks,  
Figure 4: The architecture of fine-tuned image generation. The Transformer block used therein is the same as in Figure 3.  
and the SwinIR is a relatively mature Transformer structure approach. For the purpose of comparison, we will refer to the model used in the first stage with supervised training as Stage1 and the model used in the second stage with unsupervised fine-tuning training as Stage2. Denoising experiments were carried out on SET12, which comprises 12 grayscale images. This set includes 7 images sized 256 \(\times\) 256 (Cameraman, House, Pepper, Fishistar, Monarch, Airplane, Parrot) and 5 images sized 512 \(\times\) 512 (Lena, Barbara, Boat, Man, Couple). Additionally, we used BSD68, a dataset containing 68 images randomly chosen from BSD500, characterized by rich texture details. Urban100 consists of 100 high-resolution images featuring a variety of real-world structures, including indoor, urban, and architectural scenes. These diverse and complex structures make it particularly suitable for noise reduction tasks. For our experiment, we randomly selected 30 images from this dataset. SIDD comprises approximately 30,000 noisy images captured across 10 different scenes under varying lighting conditions, using a selection of five representative mobile phones. This dataset is commonly utilized for training and evaluating real-world image denoising algorithms. Due to the large size of the images in this dataset, we performed cropping during our experiments. Specifically, we employed 10 randomly chosen images for the denoising experiments. For visualization purposes, Figure 5 displays all 12 images of SET12 used in our experiments, while Figures 6 and 7 showcase 10 representative images, each from the BSD68 and Urban100 datasets, respectively. Additionally, Figure 8 presents 10 representative images from the SIDD dataset. To objectively assess the performance of our denoising methods, we utilized the widely accepted PSNR metric to evaluate the denoising outcomes across different methods. The experiments were conducted using PyTorch 1.7 on a single NVIDIA RTX 3090 GPU, whose manufacturer is NVIDIA company and was made in Santa Clara, USA and a Lenovo desktop equipped with a 2.1 GHz Intel Core i7-6700k CPU and 16 GB of RAM.