Under specific environmental conditions such as dense fog or high dust levels, conventional RGB imaging technology faces significant challenges in capturing clear images. In contrast, infrared imaging technology, due to its unique characteristics, can effectively acquire images under these adverse conditions. However, the high cost associated with improving image quality through hardware enhancements in infrared imaging makes software-based image quality improvement crucial. Recent studies have demonstrated that deep learning networks hold significant potential for enhancing the quality of super-resolution images. To address the issues of gradient vanishing, insufficient feature utilization, and feature redundancy present in deep learning networks, this paper proposes a dual-channel hybrid convolutional residual network based on CNN with super-resolution of infrared images, which combines dual-feature extraction and dense linking. The network employs channel splitting to effectively reduce feature redundancy and leverages residual and mixed convolution techniques to enhance feature extraction and utilization. This approach efficiently preserves image details while eliminating noise. Comparative analysis using qualitative and quantitative metrics demonstrates the effectiveness of the proposed network for infrared image super-resolution tasks. The effectiveness of the network proposed in this paper in the task of super-resolution of infrared images is demonstrated by comparing it with other methods in terms of qualitative and quantitative metrics.

In the field of computer vision, image denoising remains a fundamental and challenging problem, playing a crucial role in the preprocessing of various image processing tasks. The introduction of Convolutional Neural Networks (CNNs) into the image denoising domain has yielded significant improvements across different levels of visual tasks. In recent years, models based on the Swin Transformer have also been applied to the image denoising field, demonstrating superior denoising performance that surpasses CNN-based methods, thus becoming advanced techniques in current image denoising research. This paper proposes a Swin-Conv module that combines the local modeling capabilities of residual convolutional layers with the non-local modeling capabilities of the Swin Transformer and integrates this module into the UNet architecture for image denoising. For the dataset used in the model training process, data augmentation techniques were employed to randomly enhance the dataset, thereby improving overall robustness. The results indicate that the proposed Swin Transformer Residual Conv U-Net model shows improvement over current advanced networks, achieving PSNR and SSIM values of 36.09 and 0.963 at $\sigma=15,33.87$ and 0.915 at $\sigma=25$, and 28.96 and 0.810 at $\sigma=50$.