To solve the problems of image blur smoothing, texture distortion, and excessively large parameters in real-world infrared-image recovery algorithms, a global-local attention-guided super-resolution reconstruction algorithm for infrared images is proposed. First, a cross-scale global-local feature fusion module utilizes multi-scale convolution and a transformer to fuse information at different scales in parallel and to guide the effective fusion of global and local information by learnable factors. Second, a novel domain randomization degradation model accommodates the degradation domain of real-world infrared images. Finally, a new hybrid loss based on weight learning and regularization penalty enhances the recovery capability of the network while speeding up convergence. Test results on classical degraded images and real-world infrared images show that, compared with existing methods, the images recovered by the proposed algorithm have more realistic textures and fewer boundary artifacts. Moreover, the total number of parameters can be reduced by up to 20%.