To mitigate the impact of clouds on sea surface texture analysis in marine remote sensing images, this paper studies the removal of cloud interference using an information entropy-low-pass-filter combined mask. Initially, we analyze the fundamental principles and limitations of the existing remote sensing image declouding algorithms, highlighting their unsuitability for applications requiring high fidelity. Subsequently, we propose a cloud interference removal technology based on information entropy-low-pass filtering combined mask. This technology encompasses destriping procedures with improved moment matching for remote sensing images, local information entropy filtering, and joint low-frequency filtering as correction parameters for each pixel in the images. The algorithm is characterized by low complexity and high time efficiency. Experimental results demonstrate that, compared to existing algorithms, the proposed method significantly enhances texture detail information in thin cloud areas and cloud edges while maintaining low computational complexity. It achieves an image information entropy over 7.8, a contrast ratio exceeding 60, and a mean gradient above 200. In comparisons of image details, the proposed algorithm enhances texture details without introducing artifacts or non-uniformities, thereby meeting the high-fidelity requirements for remote sensing applications.