College of Computer Science, Sichuan University, Chengdu 610064, China.
IEEE Trans Image Process. 2013 Jan;22(1):70-9. doi: 10.1109/TIP.2012.2214047. Epub 2012 Aug 17.
A novel filter is proposed for edge-preserving decomposition of an image. It is different from previous filters in its locally adaptive property. The filtered image contains local means everywhere and preserves local salient edges. Comparisons are made between our filtered result and the results of three other methods. A detailed analysis is also made on the behavior of the filter. A multiscale decomposition with this filter is proposed for manipulating a high dynamic range image, which has three detail layers and one base layer. The multiscale decomposition with the filter addresses three assumptions: 1) the base layer preserves local means everywhere; 2) every scale's salient edges are relatively large gradients in a local window; and 3) all of the nonzero gradient information belongs to the detail layer. An effective function is also proposed for compressing the detail layers. The reproduced image gives a good visualization. Experimental results on real images demonstrate that our algorithm is especially effective at preserving or enhancing local details.
提出了一种新的滤波器，用于图像的边缘保持分解。它与以前的滤波器在局部自适应特性上有所不同。滤波后的图像在任何地方都包含局部平均值，并保留局部显著边缘。将我们的滤波结果与其他三种方法的结果进行了比较。还对滤波器的行为进行了详细分析。提出了一种使用该滤波器进行多尺度分解的方法，用于处理高动态范围图像，该图像具有三个细节层和一个基础层。使用滤波器的多尺度分解满足三个假设：1）基础层在任何地方都保留局部平均值；2）每个尺度的显著边缘都是局部窗口中的相对较大梯度；3）所有非零梯度信息都属于细节层。还提出了一种有效的函数来压缩细节层。再现的图像给出了很好的可视化效果。对真实图像的实验结果表明，我们的算法在保留或增强局部细节方面特别有效。
