基于多相组重建的航空图像超分辨率算法

Super resolution of aerial image by means of polyphase components reconstruction

为提高航空图像的空间分辨率,提出一种基于多相组重建的超分辨率算法.融合图像间的互补信息,将多帧低分辨率图像作为图像基,参考帧分解为多相组,利用差异采样特性构建图像基与参考帧之间的的多相组线性关系重建得到高分辨率图像的多项组,经图像多相分解逆变换获得融合的高分辨率图像.根据该融合图像的局部内容和结构信息自适应调整控制核核函数,应用改进的控制核回归算法去除图像模糊和噪声得到清晰的超分辨率图像.与传统算法相比,该算法无需图像配准和迭代过程,计算效率极大地提高.实验结果表明,本文算法能够有效提高航空图像的空间分辨率,在定量评价指标和主观视觉效果方面都有显著提高.

Multi-frame super resolution reconstruction is a technology for obtaining a high resolution image from a set of blurred and aliased low resolution images. The most popular and widely used super resolution methods are motion based. However, the estimation of motion information （registration） is very dicult, computationally expensive and inaccurate, especially for aerial image. The sub-pixel registration error restricts the performance of the subsequent super resolution. Instead of trying to parameterize the motion estimation model, this paper proposes an image super resolution framework based on the polyphase components reconstruction algorithm and an improved steering kernel regression algorithm. Given an image observation model, a reversible 2D polyphase decomposition, which breaks down a high resolution image into polyphase components, is obtained. Though the assumption of diversity sampling, this paper adopts a fundamentally different approach, in which the low-resolution frames is used as the basis and the reference frame as the reference sub-polyphase component of the high resolution image for recovering the polyphase components of the high resolution image. The polyphase components, which fuse the low resolution frames with the complementary details, can be obtained by computing their expansion coecients in terms of this basis using the available sub-polyphase components and then inversely transforming them into a high resolution image. This paper accomplishes this by formulating the problem as the maximum likelihood estimation, which guarantees a close-to-perfect solution. Furthermore, this paper proposes an improved steering kernel regression algorithm, to help restore the fusion image with mild blur and random noise. This paper adaptively refines the steering kernel regression function according to the local region context and structures. Thus, this new algorithm not only effectively combines denoising and deblurring together, but also preserves the edge information. Our framework develops an ecient and stable algorithm to tackle the huge size and ill-posedness of the super resolution problem, and improves the computational eciency via avoiding registration and iterative computation. Several experimental results on synthetic data illustrate that our method outperforms the state-of-the-art methods in quantitative and qualitative comparisons. The proposed super resolution algorithm can indeed reconstruct high-frequency information which is otherwise unavailable in the single LR image. It can effectively suppress blur and noise, and produce visually pleasing resolution enhancement in aerial images.