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1、结构识别引导下的纹理抑制图像平滑 邵欢 傅辛易 刘春晓 伍敏 龚辰 余宗杰 浙江工商大学计算机与信息工程学院 浙江工商大学管理工程与电子商务学院 摘 要： 目的 针对目前已有的纹理平滑方法难以在抑制强梯度和尺度变化纹理的同时保持完整结构的问题, 提出一种结构识别引导下的纹理抑制图像平滑算法。方法 首先, 结构与纹理的根本区别在于重复模式, 结构应该是稀疏的, 而纹理应该是一个有重复模式的区域, 因此, 通过对结构/纹理的多尺度分析, 提取了对于结构/纹理具有辨别力的多尺度内变差特征;然后, 借助支持向量机, 对提取的特征样本点训练出一个结构/纹理分类器;就分类结果中存在的结构较粗、毛刺等问题,
2、 进一步对分类结果进行细化和剔除毛刺与孤立点的后处理操作, 以获得最终的更为精细的结构识别结果;最后, 提出结构引导下的自适应双边图像滤波算法, 达到既能抑制强梯度和尺度变化的纹理又能保持结构完整性的图像平滑效果。结果 本文提出的多尺度内变差特征在支持向量机训练中达到了 96.12%的正确率, 结构引导下的图像滤波能够在保持结构的同时, 有效地抑制强梯度和尺度变化的纹理细节。结论本文算法在兼顾结构的保持和强梯度以及尺度变化纹理的抑制方面超越了已有的方法, 对于结构提取、细节增强、图像分割、色调映射、图像融合和目标识别等众多技术领域的发展将具有较强的促进作用, 体现了潜在的实际应用价值。关键词：
3、 纹理抑制; 结构识别; 多尺度内变差; 强梯度纹理; 尺度变化; 作者简介：邵欢 (1993) , 男, 现为浙江工商大学计算机技术专业硕士研究生, 主要研究方向为图像处理与模式识别。E-mail:作者简介：刘春晓, 副教授, 硕士生导师, E-mail:收稿日期：2017-05-09基金：浙江省自然科学基金项目 (LY14F020004) Structure recognition guided texture suppressing image smoothingShao Huan Fu Xinyi Liu Chunxiao Wu Min Gong Chen Yu Zongjie Sch
4、ool of Computer Science & Information Engineering, Zhejiang Gongshang University; School of Management and E-Business, Zhejiang Gongshang University; Abstract： Objective Natural scenes generally contain different scale objects and textures, which carry rich information in regard to human perception.
5、 Texture usually signifies pixel values, which change with high frequency. Generally, images are composed of many important structures, texture, edges, etc. Therefore, mining the meaningful structure from textures or complex background images is a critical task in vision processing. The core of imag
6、e smoothing lies in the separation of structure and texture. Effective preservation of the structure while suppressing the texture with strong gradient or var-ying scales is a challenging problem. Most of the existing image smoothing methods tends to deal with weak gradient texture images; if the te
7、xture gradient is strong, then these methods will fail. To solve the abovementioned problem, a structure recognition guided texture smoothing algorithm is proposed, which deals with the structure and the texture separately and detect structure before image smoothing. Method First, this paper argues
8、that the fundamental difference between structure and texture is the repetition pattern. Particularly, the structure should be sparse and the texture should be a region with a repeating pattern. According to this characteristic, the discriminative features for distinguishing between structure and te
9、xture are designed and extracted based on the multi-scale analysis of inherent variation. At least two reasons are available for presenting the multi-scale approach. One reason is that structure and texture are relative. When the scale is small, the texture may not show up, and thus the scale needs
10、to be enlarged and the essence of the texture is released.The other reason is that the texture in the image is diverse, and the adaptive scale in different regions is difficult. Furthermore, textures with various attributes may exist in the same image, a single scale can only solve the partial textu
11、re with the default scale parameter and the recognition of other textures will lose. Therefore, multi-scale analysis of inherent variation is proposed to ensure that different textures can display their own repetitive pattern attributes. Second, the core part in the field of pattern recognition is f
12、eature extraction. Therefore, the feature extracted must be more robust to guarantee the discrimination ability is strong enough and the stability is good enough. To obtain more accurate features, we need to consider the multi-scale inherent variation in the macroscopic view and grasp its general ru
13、les. After we analyze the trend of multiscale inherent variation curves at different pixel locations, several discriminative features are extracted. Then, these features can be used for subsequent structural recognition. We regard the separation of texture and structure as a typical twoclass issue,
14、and the support vector machine is a classical two-class classification method. Compared with many existent machine learning methods, it is a relatively lightweight classifier, which can obtain desirable classification results without a large sample. Consequently, this paper prefers to use the suppor
15、t vector machine to distinguish the texture and structure, with the help of support vector machine, a classifier is trained with the extracted feature pixels, and utilized to classify structure and non-structure pixels efficiently. However, due to the block effect in edge compression and the computa
16、tional mechanism of inherent variation, pixels nearby the structure will always be affected by the real structure and its multi-scale inherent variation curve is similar in structure. Hence, the support vector machine classification results cannot reach a single pixel. We observed large amounts of data and find that the non-structured pixel appeared symmetrically on both sides of the window. Although the support vector machine classifica
