# Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images
## 4 Experiments
### Ablation Studies  
To illustrate the efficacy of our proposed degradation and spatially correlative loss function, this chapter presents ablation studies conducted on the baseline network of Real-ESRGAN. In the ablation studies, spatially correlative loss was first embedded into the baseline network without modifying the degradation process. For the degradation component, a gate mechanism and random shuffle strategy were individually integrated into the baseline network for separate evaluations, and then the degradation methods of the gate mechanism and random shuffle strategy were jointly embedded into the baseline network. Finally, both the spatially correlative loss and our degradation were embedded into the baseline network, resulting in the Inf-OSRGAN model. The experimental results are shown in Figure 7, with the best results highlighted with stars. The ablation studies followed the same experimental settings as the main experiment. It can be observed in Figure 7 that, compared to the baseline, the proposed Inf-OSRGAN led to an improvement, particularly in the degradation. Specifically, when only the random shuffle was added, the PSNR increased from 27.89 to 27.92. When only the gate mechanism was added, the PSNR increased from 27.89 to 29.70. When both the gate mechanism and random shuffle were added, the PSNR improved to 29.95. The optimization of metrics could also be observed in terms of SSIM and FID. Figure 6: This figure presents a side-by-side comparison of our proposed model, Inf-OSRGAN, against several established methods: RCAN, SRGAN, RealSR, and Real-ESRGAN. In the first group of images, particular attention is drawn to the bridge pier. In the second group, the focus shifts to the textures of the pillars on the ship and the fabric. Figure 7: Quantitative results of the ablation studies. The best results are highlighted with stars. The left vertical axis represents the average PSNR and FID values, while the average SSIM is indicated using the right vertical axis. Low-resolution (LR) images were obtained through interpolation from the original images. When there was a rich degradation space, the super-resolution algorithms could recover more high-frequency details and textures from the low-resolution inputs, thereby improving the image quality. The improvement in super-resolution effect could be reflected through the metrics considering pixel-level, feature distribution, brightness, and contrast. Therefore, these metrics could effectively evaluate the improvement in super-resolution algorithms resulting from a rich degradation space. Upon the integration of the spatially correlative loss function, there was a marginal reduction in PSNR, FID, and SSIM values. The introduction of spatially correlative loss in super-resolution tasks enhances the structural integrity of the reconstructed images, leading to a more uniform and visually cleaner output. Since the optimization goal of spatially correlative loss is not entirely consistent with traditional metrics, they may decrease. Spatially correlative loss prioritizes the optimization of image structure rather than pixel-level similarity. This approach aids in preserving the structural consistency of the image, emphasizing enhancing details and textures. Consequently, it yields a clearer and more visible image structure but also tends to smooth out the noise signal. The traditional metrics primarily focus on the pixel-level similarity and statistical features of an image, rather than maintaining the structural consistency. Therefore, when introducing spatially correlative loss, the reconstructed image's structure may be more pronounced, but a decrease in comparison with traditional metrics may be observed. This does not imply a decline in the actual visual quality and enhancement of details. In fact, enhancing spatial consistency can improve the structure and details of an image, leading to improved perceptual quality. Spatially correlative loss can effectively handle texture details and high-frequency information.