Fekri F, Mersereau R M, Schafer R W
Center for Signal and Image Process., Georgia Inst. of Technol., Atlanta, GA 30332-0250, USA.
IEEE Trans Image Process. 2000;9(7):1272-81. doi: 10.1109/83.847839.
This paper presents an approach for the effective combination of interpolation with binarization of gray level text images to reconstruct a high resolution binary image from a lower resolution gray level one. We study two nonlinear interpolative techniques for text image interpolation. These nonlinear interpolation methods map quantized low dimensional 2 x 2 image blocks to higher dimensional 4 x 4 (possibly binary) blocks using a table lookup operation. The first method performs interpolation of text images using context-based, nonlinear, interpolative, vector quantization (NLIVQ). This system has a simple training procedure and has performance (for gray-level high resolution images) that is comparable to our more sophisticated generalized interpolative VQ (GIVQ) approach, which is the second method. In it, we jointly optimize the quantizer and interpolator to find matched codebooks for the low and high resolution images. Then, to obtain the binary codebook that incorporates binarization with interpolation, we introduce a binary constrained optimization method using GIVQ. In order to incorporate the nearest neighbor constraint on the quantizer while minimizing the distortion in the interpolated image, a deterministic-annealing-based optimization technique is applied. With a few interpolation examples, we demonstrate the superior performance of this method over the NLIVQ method (especially for binary outputs) and other standard techniques e.g., bilinear interpolation and pixel replication.
本文提出了一种将灰度文本图像的插值与二值化有效结合的方法，以便从低分辨率灰度图像重建高分辨率二值图像。我们研究了两种用于文本图像插值的非线性插值技术。这些非线性插值方法使用查表操作将量化的低维2×2图像块映射到高维4×4（可能是二值的）块。第一种方法使用基于上下文的非线性插值矢量量化（NLIVQ）执行文本图像的插值。该系统具有简单的训练过程，并且（对于灰度高分辨率图像）性能与我们更复杂的广义插值矢量量化（GIVQ）方法相当，后者是第二种方法。在该方法中，我们联合优化量化器和插值器，以找到低分辨率和高分辨率图像的匹配码本。然后，为了获得将二值化与插值相结合的二值码本，我们引入了一种使用GIVQ的二值约束优化方法。为了在最小化插值图像失真的同时在量化器上纳入最近邻约束，应用了一种基于确定性退火的优化技术。通过几个插值示例，我们证明了该方法相对于NLIVQ方法（特别是对于二值输出）和其他标准技术（例如双线性插值和像素复制）的优越性能。
