# Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images
## 3 Method
### Degradation Model
#### 3.1.2 The Proposed Degradation Model  
The proposed degradation model aims to reduce computational complexity, while enriching the degradation space. In the degradation process, a gate mechanism and random shuffle strategy are introduced into the second-order degradation model of Real-ESRGAN. Additionally, based on the characteristics of the experimental data and infrared devices, JPEG compression was not performed. This decision was made because the experimental data were stored in a single-channel format, while JPEG compression was primarily used for compressing color information in RGB images. Moreover, in practical scenarios, infrared devices can always export in lossless BMP format, thereby minimizing the need for JPEG compression of their images. Consequently, JPEG compression was excluded from the degradation process. The architecture of the proposed degradation model is illustrated in Figure 2.  
The high-resolution (HR) image undergoes a two-stage degradation process to obtain the low-resolution (LR) image. In Equation (5), \(\mathcal{D}_{1}\) and \(\mathcal{D}_{2}\) represent the first-order and second-order degradation processes, respectively. The LR image can be obtained by  
\[I^{LR}=(\mathcal{D}_{1}\circ\mathcal{D}_{2})(I^{HR}). \tag{5}\]  
In each round of degradation, we perform a random shuffling of the degradation sequence \(\{Blur,Noise,Resize\}\). We use \(\mathbf{D}_{n}\) to represent the basic degradation, where \(n\) represents the corresponding sequence. Here, in Equation (6), \(I^{IN}\) denotes the input image, and \(I^{OUT}\) represents the output image.  
\[I^{OUT}=\mathbf{D}(I^{IN})=(\sigma_{1}(\mathbf{D}_{1})\circ\sigma_{2}( \mathbf{D}_{2})\circ\sigma_{3}(\mathbf{D}_{3})\circ\cdots\circ\sigma_{n}( \mathbf{D}_{n}))(I^{IN}). \tag{6}\]  
For example, for a specific shuffling order \(\{3,1,2\}\), the proposed degradation model processes the image as follows:  
Step 1: Resize-\(\mathbf{D}_{3}\), \(I^{OUT1}=\sigma_{1}(\mathbf{D}_{3})I^{IN}\)  
Step 2: Blur-\(\mathbf{D}_{1}\), \(I^{OUT2}=\sigma_{2}(\mathbf{D}_{1})I^{OUT1}\)  
Step 3: Noise-\(\mathbf{D}_{2}\), \(I^{OUT}=\sigma_{3}(\mathbf{D}_{2})I^{OUT2}\)  
Each order generates a random list \(\{\sigma_{1},\sigma_{2},\ldots,\sigma_{n}\}\), which determines whether is \(\mathbf{D}_{i}\) executed. If the value \(\sigma_{i}\) is 0, the corresponding degradation process is not performed. If the \(\sigma_{i}\) is 1, the degradation process is executed. To ensure the complexity of degradation, in our experiments the random list \(\{\sigma_{1},\sigma_{2},\ldots,\sigma_{n}\}\) was not all filled with 0 s, meaning that at least one degradation process was performed.  
The blur degradation uses both isotropic and anisotropic Gaussian noise. The noise degradation applies Gaussian noise and Poisson noise. For the resize degradation, the image is randomly resized using area, bilinear, and bicubic interpolation.  
By combining a gate mechanism and random shuffle strategy, the proposed degradation model can flexibly handle different basic degradation types and generate diverse degradation patterns. Such a strategy can better simulate diverse degradation situations in the real world and improve the adaptability and robustness of the model.  
Figure 2: The proposed degradation model integrates a gate mechanism and a random shuffle strategy based on second-order degradation. The degradation model performs second-order degradation, applying the shuffle strategy at each stage, while incorporating a gate controller into each degradation operation.