Infrared remote sensing images are often degraded by blur and stripe noise caused by satellite attitude variations, optical distortions, and electronic interference, which significantly compromise image quality and target detection performance. Existing joint deblurring and destriping methods tend to over-smooth image edges and textures, failing to effectively preserve high-frequency details and sometimes misclassifying ringing artifacts as stripes. This paper proposes a variational framework for simultaneous deblurring and destriping of infrared remote sensing images. By leveraging an adaptive structure tensor model, the method exploits the sparsity and directionality of stripe noise, thereby enhancing edge and detail preservation. During blur kernel estimation, a fidelity term orthogonal to the stripe direction is introduced to suppress noise and residual stripes. In the image restoration stage, a WCOB (Non-blind restoration based on Wiener-Cosine composite filtering) model is proposed to effectively mitigate ringing artifacts and visual distortions. The overall optimization problem is efficiently solved using the alternating direction method of multipliers (ADMM). Extensive experiments on real infrared remote sensing datasets demonstrate that the proposed method achieves superior denoising and restoration performance, exhibiting strong robustness and practical applicability.