目的 自动指纹识别系统大多是基于细节点匹配的，系统性能依赖于输入指纹质量。输入指纹质量差是目前自动指纹识别系统面临的主要问题。为了提高系统性能，实现对低质量指纹的增强，提出了一种基于多尺度分类字典稀疏表示的指纹增强方法。方法 首先，构建高质量指纹训练样本集，基于高质量训练样本学习得到多尺度分类字典；其次，使用线性对比度拉伸方法对指纹图像进行预增强，得到预增强指纹；然后，在空域对预增强指纹进行分块，基于块内点方向一致性对块质量进行评价和分级；最后，在频域构建基于分类字典稀疏表示的指纹块频谱增强模型，基于块质量分级机制和复合窗口策略，结合频谱扩散，基于多尺度分类字典对块频谱进行增强。结果 在指纹数据库FVC2004上将提出算法与两种传统指纹增强算法进行了对比实验。可视化和量化实验结果均表明，相比于传统指纹增强算法，提出的方法具有更好的鲁棒性，能有效改善低质量输入指纹质量。结论 通过将指纹脊线模式先验引入分类字典学习，为拥有不同方向类别的指纹块分别学习一个更为可靠的字典，使得学习到的分类字典拥有更可靠的脊线模式信息。块质量分级机制和复合窗口策略不仅有助于频谱扩散，改善低质量块的频谱质量，而且使得多尺度分类字典能够成功应用，克服了增强准确性和抗噪性之间的矛盾，使得块增强结果更具稳定性和可靠性，显著提升了低质量指纹图像的增强质量。

Objective Most automatic fingerprint identification systems (AFISs) are based on minutiae matching. The accuracy and reliability of minutiae extraction are largely dependent on the quality of the input fingerprint image. Thus, the performance of these AFISs is largely determined by the quality of input fingerprint images. In practice, the quality of a fingerprint image may suffer from various impairments, and the image may appear with ridge adhesions, ridge fractures, or uneven contrast. To improve the performance of AFISs, the quality of fingerprint images must be enhanced. This study proposes a novel fingerprint enhancement algorithm that uses sparse representation by multi-scale classification dictionaries.Method First, we sample high-quality training fingerprints to build the training set for multi-scale classification dictionaries learning, and the multi-scale classification dictionaries are learned from the training set. A crucial issue in enhancing fingerprint images is obtaining an effective prior or constraint. Unlike generic images, fingerprint images have a steady and reliable ridge pattern. To obtain an effective prior or constraint, fingerprint patch orientations are estimated by weighted linear projection analysis (WLPA) on the basis of the vector set of point gradients. We classify training samples with the same size into eight groups according to their ridge orientation pattern. Instead of simply learning a dictionary, we learn a classification dictionary for each class with the same size. Second, fingerprints are pre-enhanced using the linear contrast stretching method. The sparse grey space in the fingerprint image is used, and the fingerprint image contrast can be stretched to cover the entire greyscale space. Consequently, the gray level information of the input fingerprint can be preserved against loss, and contrast enhancement can be improved. Contrast enhanced fingerprint contributes to the subsequent enhancement. Third, a fingerprint has a unique natural pattern, which is suitable for frequency-domain analysis. Generally, a good frequency-domain fingerprint enhancement approach is designed to work on spatial partitioning and frequency-domain enhancement. Thus, the fingerprint is partitioned into patches in the spatial domain on the basis of a non-overlapping window, the orientations of fingerprint patches are estimated by WLPA, and the qualities of the patches are evaluated and classified by the coherence of the point orientations. Finally, the fingerprint patches are transformed to the frequency domain by 2D discrete Fourier transform. The enhancement model of the patch spectrum is constructed via sparse representation modeling using classification dictionaries. The patch spectra are enhanced on the basis of a quality grading scheme and a composite strategy using multi-scale classification dictionaries learning combined with spectra diffusion. The fingerprint patch is enhanced according to its own priority, and patches with higher quality are enhanced when patches with lower quality are enhanced. Multi-scale classification dictionaries learning ensure the reliability of the enhancement. Spectra diffusion is successfully applied with the help of the quality grading and neighborhood priority scheme and the composite window strategy, and it can improve the quality of the patch spectra with low quality. Spectra diffusion provides accurate ridge spectra information for lower quality patches, thus ensuring the reliability of the ridge spectra for multi-scale classification dictionaries enhancing.Result The proposed method is implemented and tested on fingerprint images from FVC2004. Some visual experiments and performance evaluations of minutiae extractions are illustrated. We compare our method with state-of-the-art fingerprint enhancement methods and report that the proposed method is superior in enhancing fingerprint images. Experimental results demonstrate that low-quality fingerprints can be effectively enhanced by the proposed method. Compared with traditional fingerprint enhancement algorithms, the proposed method is more robust against noise and exhibits a more prominent effect on low-quality fingerprint images.Conclusion By introducing ridge pattern priori into a classification dictionary, a classification dictionary for each class with the same size is learned. Classification dictionaries based on the ridge pattern constraint can capture a reliable ridge pattern prior. Using classification dictionaries improves the effectiveness of the sparse modeling of information in a fingerprint patch. The quality grading scheme and the composite window strategy are adopted to assist the multi-scale dictionary in overcoming the contradiction between accuracy and anti-noise capability. Furthermore, the combination of composite window and quality evaluation ensures that spectra diffusion is successfully applied. The proposed method significantly improves the quality of low-quality input fingerprints.