Hewlett-Packard Co., Palo Alto, CA.
IEEE Trans Image Process. 1995;4(4):486-98. doi: 10.1109/83.370677.
Two different approaches in the inverse halftoning of error-diffused images are considered. The first approach uses linear filtering and statistical smoothing that reconstructs a gray-scale image from a given error-diffused image. The second approach can be viewed as a projection operation, where one assumes the error diffusion kernel is known, and finds a gray-scale image that will be halftoned into the same binary image. Two projection algorithms, viz., minimum mean square error (MMSE) projection and maximum a posteriori probability (MAP) projection, that differ on the way an inverse quantization step is performed, are developed. Among the filtering and the two projection algorithms, MAP projection provides the best performance for inverse halftoning. Using techniques from adaptive signal processing, we suggest a method for estimating the error diffusion kernel from the given halftone. This means that the projection algorithms can be applied in the inverse halftoning of any error-diffused image without requiring any a priori information on the error diffusion kernel. It is shown that the kernel estimation algorithm combined with MAP projection provide the same performance in inverse halftoning compared to the case where the error diffusion kernel is known.
考虑了两种不同的误差扩散图像反半调处理方法。第一种方法使用线性滤波和统计平滑，从给定的误差扩散图像重建灰度图像。第二种方法可以看作是一种投影操作，假设已知误差扩散核，并找到将半色调成相同二值图像的灰度图像。开发了两种投影算法，即最小均方误差（MMSE）投影和最大后验概率（MAP）投影，它们在执行反量化步骤的方式上有所不同。在滤波和两种投影算法中，MAP 投影在反半调处理中提供了最佳性能。通过自适应信号处理技术，我们提出了一种从给定的半色调图像中估计误差扩散核的方法。这意味着，在没有关于误差扩散核的任何先验信息的情况下，投影算法可以应用于任何误差扩散图像的反半调处理。结果表明，与已知误差扩散核的情况相比，核估计算法与 MAP 投影相结合在反半调处理中提供了相同的性能。
