# CA-BSN: Mural Image Denoising Based on Cross-Attention Blind Spot Network
## 4 Experiments
### Dataset and Evaluation Metric  
Our approach is evaluated on two real-world datasets, SIDD and DND, which are mainstreams in image denoising. The SIDD is a set of about 30,000 noise-containing images obtained from five representative smartphone cameras in ten scenes under different lighting conditions, along with the corresponding clean images. We selected sRGB images from SIDD-Medium with 320 noisy-clean pairs for model training. We use sRGB images from the SIDD validation set and benchmarks with 40 images per category, each of which can be cropped to 1280 image blocks of 256 \(\times\) 256 size, for validation and evaluation. The DND consists of 50 noisy images, including indoor and outdoor scenes, there are no clear images, and the denoising results can only be obtained by an online system. Since our method does not need to consider clean images, the DND can be used directly as a training and test set.  
Our method belongs to a kind of unsupervised denoising, self-supervised denoising. In order to better adapt the model to mural images and demonstrate the denoising effect of CA-BSN on mural images, we construct a small mural dataset. We retain the mural dataset used in the lab in the past and obtained some electronic images by collecting classic books and official museum displays, and extended the dataset by cropping, rotating, and flipping, which are the ground-truth data.  
In order to obtain random and natural noisy images, we analyze the noise characteristics of the images, which are mainly characterized by irregular distribution and size, spatial correlation of noise, and mixing of multiple types of noise. Therefore, we chose to add Gaussian noise, Poisson noise and Perlin noise. The specific formulas are as follows:  
\[y_{T-1}=x_{T-1}+\alpha N(\mu,\sigma^{2})+(1-\alpha)Poisson(\lambda) \tag{14}\]  
\[x_{T}=y_{T-1}+Perlin(scale,octaves,persistence,lacunarity) \tag{15}\]  
where \(T\) denotes the number of times the noise was added, \(y\) is an intermediate variable, and \(x_{T}\) is the data obtained after \(x_{T-1}\) has been added by the Gaussian, Poisson, and Perlin noise. Item \(\alpha\) is the random mixing ratio of Gaussian and Poisson noise ranging from 0.3 to 0.7. \(N\big{(}\mu,\sigma^{2}\big{)}\) denotes the Gaussian noise with mean \(\mu\) and standard deviation \(\sigma\). The mean value ranges from \(-1.0\) to \(1.0\) and standard deviation from 9 to 25. \(Poisson(\lambda)\) denotes Poisson noise with intensity \(\lambda\) and a random range of intensity from 8 to 12. \(Perlin(scale, octaves,persistence,lacunarity)\) denotes the equation for two-dimensional Perlin noise distribution, where the scale has a random range of 8 to 12, octaves of 4 to 8, persistence between 0.3 and 0.7, and lacunarity of 1.5 to 3.0. By superimposing the noise on each image several times, we obtain a batch of mural noise images that satisfy the image noise properties but are still natural.  
Since our method does not use ground truth data, we directly use the noisy mural dataset for training and testing. The ground truth data are used to compare metrics. The mural images in our paper are sRGB data containing 15,000 patches of size 224 \(\times\) 224, mainly Buddhist culture and ancient scenes. Partial samples of the dataset are shown in Figure 9.  
In order to verify the denoising effect of the model, we use the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM) as objective evaluation metrics. PSNR is used to measure the quality of the image, and SSIM measures the similarity of the two images. Higher PSNR and SSIM values indicate improved output image quality.