Signal Processing Laboratory (LTS4), Institute of Electrical Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
IEEE Trans Image Process. 2011 Nov;20(11):3151-62. doi: 10.1109/TIP.2011.2144609. Epub 2011 Apr 25.
This paper addresses the reconstruction of high-resolution omnidirectional images from multiple low-resolution images with inexact registration. When omnidirectional images from low-resolution vision sensors can be uniquely mapped on the 2-sphere, such a reconstruction can be described as a transform-domain super-resolution problem in a spherical imaging framework. We describe how several spherical images with arbitrary rotations in the SO(3) rotation group contribute to the reconstruction of a high-resolution image with help of the spherical Fourier transform (SFT). As low-resolution images might not be perfectly registered in practice, the impact of inaccurate alignment on the transform coefficients is analyzed. We then cast the joint registration and super-resolution problem as a total least-squares norm minimization problem in the SFT domain. A l(1)-regularized total least-squares problem is considered and solved efficiently by interior point methods. Experiments with synthetic and natural images show that the proposed methods lead to effective reconstruction of high-resolution images even when large registration errors exist in the low-resolution images. The quality of the reconstructed images also increases rapidly with the number of low-resolution images, which demonstrates the benefits of the proposed solution in super-resolution schemes. Finally, we highlight the benefit of the additional regularization constraint that clearly leads to reduced noise and improved reconstruction quality.
本文讨论了如何从具有不精确配准的多个低分辨率图像重建高分辨率全景图像。当来自低分辨率视觉传感器的全景图像可以唯一地映射到 2-球面上时，这种重建可以在球形成像框架中描述为变换域超分辨率问题。我们描述了如何使用球面傅里叶变换 (SFT) ，通过 SO(3)旋转群中的任意旋转的几个球面图像来帮助重建高分辨率图像。由于在实际中低分辨率图像可能无法完全配准，因此分析了不准确对准对变换系数的影响。然后，我们将联合配准和超分辨率问题作为 SFT 域中的全最小二乘范数最小化问题来处理。考虑并通过内点方法有效地解决了 l(1)正则化的全最小二乘问题。使用合成和自然图像进行的实验表明，即使在低分辨率图像中存在较大的配准误差的情况下，所提出的方法也可以有效地重建高分辨率图像。重建图像的质量也随着低分辨率图像数量的增加而迅速提高，这证明了该解决方案在超分辨率方案中的优势。最后，我们强调了附加正则化约束的好处，这显然可以降低噪声并提高重建质量。
