Department of Computer Science, University of California, Davis, CA 95616-8562, USA.
IEEE Trans Vis Comput Graph. 2013 Mar;19(3):446-59. doi: 10.1109/TVCG.2012.144.
Visualizing complex volume data usually renders selected parts of the volume semitransparently to see inner structures of the volume or provide a context. This presents a challenge for volume rendering methods to produce images with unambiguous depth-ordering perception. Existing methods use visual cues such as halos and shadows to enhance depth perception. Along with other limitations, these methods introduce redundant information and require additional overhead. This paper presents a new approach to enhancing depth-ordering perception of volume rendered images without using additional visual cues. We set up an energy function based on quantitative perception models to measure the quality of the images in terms of the effectiveness of depth-ordering and transparency perception as well as the faithfulness of the information revealed. Guided by the function, we use a conjugate gradient method to iteratively and judiciously enhance the results. Our method can complement existing systems for enhancing volume rendering results. The experimental results demonstrate the usefulness and effectiveness of our approach.
可视化复杂的体数据通常会使体数据的选定部分半透明化，以查看体数据的内部结构或提供上下文。这给体绘制方法带来了挑战，因为这些方法需要生成具有明确深度排序感知的图像。现有的方法使用视觉提示，如晕影和阴影，来增强深度感知。但是，这些方法会引入冗余信息并需要额外的开销，同时也存在其他限制。本文提出了一种新的方法，用于在不使用额外视觉提示的情况下增强体绘制图像的深度排序感知。我们基于定量感知模型设置了一个能量函数，以根据深度排序和透明度感知的有效性以及所揭示信息的保真度来衡量图像的质量。在该函数的指导下，我们使用共轭梯度法迭代地、明智地增强结果。我们的方法可以补充现有的增强体绘制结果的系统。实验结果证明了我们方法的有用性和有效性。
