Desolneux Agnès, Ladjal Saïd, Moisan Lionel, Morel Jean-Michel
ENS Cachan, CMLA, Cachan, France.
IEEE Trans Image Process. 2002;11(10):1129-40. doi: 10.1109/TIP.2002.804566.
We address the problem of computing a local orientation map in a digital image. We show that standard image gray level quantization causes a strong bias in the repartition of orientations, hindering any accurate geometric analysis of the image. In continuation, a simple dequantization algorithm is proposed, which maintains all of the image information and transforms the quantization noise in a nearby Gaussian white noise (we actually prove that only Gaussian noise can maintain isotropy of orientations). Mathematical arguments are used to show that this results in the restoration of a high quality image isotropy. In contrast with other classical methods, it turns out that this property can be obtained without smoothing the image or increasing the signal-to-noise ratio (SNR). As an application, it is shown in the experimental section that, thanks to this dequantization of orientations, such geometric algorithms as the detection of nonlocal alignments can be performed efficiently. We also point out similar improvements of orientation quality when our dequantization method is applied to aliased images.
我们解决了在数字图像中计算局部方向图的问题。我们表明，标准的图像灰度量化会在方向的重新分布中导致强烈偏差，从而阻碍对图像进行任何精确的几何分析。接着，我们提出了一种简单的去量化算法，该算法保留了所有图像信息，并将量化噪声转换为附近的高斯白噪声（我们实际上证明只有高斯噪声才能保持方向的各向同性）。通过数学论证表明，这会使高质量的图像各向同性得以恢复。与其他经典方法不同的是，事实证明，无需对图像进行平滑处理或提高信噪比（SNR）就能获得此属性。作为一个应用，实验部分表明，由于方向的这种去量化，诸如非局部对齐检测等几何算法能够高效执行。我们还指出，当将我们的去量化方法应用于混叠图像时，方向质量也会有类似的提升。
