# Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images
## 2 Related Works
### Deep Learning Methods  
Advances in deep neural network technology and enhanced computing power have led to deep-learning-based image super-resolution algorithms that surpass the traditional methods in terms of both objective metrics and subjective perception. Initially, these methods primarily employed convolutional neural networks for end-to-end mapping on artificially paired data, exemplified by the SRCNN [32] model, which pioneered the use of neural networks in the super-resolution task. Subsequently, researchers have made further improvements to network model design, proposing network architectures such as FSRCNN [32], ESPCNN [33], VDSR [34], SRResNet [35], RDN [36], SRDenseNet [37], DRCN [38], DRRN [39], RCAN [40], etc. These designs incorporated ideas including aspects of network depth, feature dimensions, residual information, dense stacking, and recursive structures, and effectively improved the super-resolution performance. In practice, task-based designs that rely on network training often yield more significant performance improvements. Constraints based solely on pixel loss in end-to-end mapping can result in images that appear blurry and overly smooth. However, generative models based on generative adversarial networks (GANs) [41] can mitigate this limitation. Through adversarial training between a generator and a discriminator, GAN-based models can progressively acquire the ability to generate realistic details.  
The SRGAN [35] model was the first to introduce GAN networks into the super-resolution task and proposed perceptual loss, which effectively addressed the issue of the low perceptual quality in reconstructed images, thereby enhancing the realism of the generated images. The ESRGAN model [42] introduced the RRDBNet generator network structure. However, the exclusive focus on the classic degradation process restricts its applicability in real-world scenarios. In comparison to the kernel-based degradation estimation in RealSR [43], Real-ESRGAN [44] introduced higher-order degradation processes that more accurately reflect real-world conditions. Subsequently, the BSRGAN [16] model and GD model [17] independently proposed the shuffle strategy and a gate mechanism to diversify the degradation space. However, these operations merely capture non-combinations within the degradation space and fail to establish a unified baseline degradation model.  
In the quest to enhance image details, researchers initially concentrated on optimizing the discriminator's role. The SRGAN [35] model employs a discriminator that directly assesses the authenticity of generated images, whereas the ESRGAN [42] model refines the discriminator to gauge relative realism rather than absolute values. Real-ESRGAN [44] further employs a U-Net [45] structure to discriminate the realism of images at the pixel level, effectively improving the model's generalization and performance. The AESGAN [46] model adopts a multi-scale U-Net discriminator to further sharpen details. However, in infrared super-resolution tasks that prioritize texture restoration, noise interference can exacerbate artifacts. Therefore, it is necessary to further constrain texture and structural information through loss functions, ensuring that the generative model converges effectively within the degradation space and demonstrates generalization in real-world scenarios.