LSIIT, UMR 7005 CNRS-Université de Strasbourg, 67412 Illkirch, France.
Med Image Anal. 2010 Aug;14(4):594-605. doi: 10.1016/j.media.2010.04.005. Epub 2010 May 6.
Image enhancement is of great importance in medical imaging where image resolution remains a crucial point in many image analysis algorithms. In this paper, we investigate brain hallucination (Rousseau, 2008), or generating a high-resolution brain image from an input low-resolution image, with the help of another high-resolution brain image. We propose an approach for image super-resolution by using anatomical intermodality priors from a reference image. Contrary to interpolation techniques, in order to be able to recover fine details in images, the reconstruction process is based on a physical model of image acquisition. Another contribution to this inverse problem is a new regularization approach that uses an example-based framework integrating non-local similarity constraints to handle in a better way repetitive structures and texture. The effectiveness of our approach is demonstrated by experiments on realistic Brainweb Magnetic Resonance images and on clinical images from ADNI, generating automatically high-quality brain images from low-resolution input.
图像增强在医学成像中非常重要，因为在许多图像分析算法中，图像分辨率仍然是一个关键问题。在本文中，我们研究了脑幻觉（Rousseau，2008），即通过另一张高分辨率的脑图像，从输入的低分辨率图像中生成一张高分辨率的脑图像。我们提出了一种利用参考图像中的解剖跨模态先验进行图像超分辨率的方法。与插值技术不同，为了能够在图像中恢复精细的细节，重建过程基于图像采集的物理模型。这个反问题的另一个贡献是一种新的正则化方法，它使用基于实例的框架集成非局部相似性约束，以更好地处理重复结构和纹理。我们的方法在对真实的 Brainweb 磁共振图像和 ADNI 的临床图像进行的实验中得到了验证，从低分辨率输入自动生成高质量的脑图像。
