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.

Multi-scale stripe noise and blur degradation in wide-field infrared remote sensing images are strongly coupled, posing significant challenges for image restoration. Most existing approaches rely on sequential processing frameworks, in which destriping and deblurring are performed independently. However, residual artifacts or over-smoothing introduced during stripe removal can contaminate subsequent blur kernel estimation, while the strong directional gradients presented by stripe noise can mislead kernel estimation. Moreover, the multi-scale characteristics of stripe noise are rarely explicitly modeled in current methods. To address these limitations, this paper proposes a multi-scale hierarchical joint variational model (MHJVM) for the simultaneous removal of blur and multi-scale stripe noise in wide-field infrared remote sensing images. A Gaussian pyramid is employed to decompose the degraded image into multiple resolution levels. At the low-resolution level, leveraging the weakened structural characteristics of stripes, a multi-scale edge-preserving operator is introduced to adjust the penalty strength, enabling the preliminary suppression of wide stripes and the initial robust estimation of the blur kernel. Second,at high-resolution levels, a blur kernel refinement model based on the anisotropy of stripe residuals is established, explicitly suppressing the contamination of stripe-oriented residuals on kernel estimation and removing fine-scale stripes at the pixel level. Finally, an adaptive ringing suppression model based on Laplacian response is incorporated into the non-blind deconvolution stage to preserve image details while mitigating ringing artifacts. Experimental results on both simulated and real satellite-borne infrared remote sensing images demonstrate that the proposed method outperforms existing sequential frameworks across diverse scenes, including urban, mountain, and sea surface areas. Under multi-scale interference conditions, it achieves an average SNR above 30 dB and an SSIM around 0.92. The method effectively suppresses multi-scale stripe noise, enables accurate blur kernel estimation, and preserves structural fidelity. These results validate the robustness and generalization capability of the proposed multi-scale collaborative optimization framework for joint destriping and deblurring under coupled degradation scenarios.