基于解模糊算法的蚕蛹图像恢复及雌雄识别

在利用机器视觉技术识别雌雄蚕蛹过程中，因蛹体为非规则椭球体所带来的空间变化模糊造成蚕蛹图像中大量细节结构特征信息丢失，这极大地降低了雌雄蚕蛹识别的准确率。针对此问题，该文提出了一种将复杂的空间变化模糊图像恢复问题化为多个简单的空间模糊图像求解的策略。首先根据蚕蛹图像的模糊图谱将图像划分为多个具有相似程度模糊的子图像区域；再利用 Lucy Richardson 方法对各子图像区域分别进行非盲反卷积解模糊；最后将恢复的各子图像进行拼合并使用双边滤波方法消除图像拼合误差，保证图像信息准确融合。试验结果表明，该算法性能与目前所公认最优的 Shen 方法相比，能够得到更好的蚕蛹图像视觉质量，蚕蛹图像质量的定量评估指标——总变差均值（TVM）平均提高了22.8%，因此，该文方法具有更优的性能，能够有效消除空间变化模糊影响，恢复出更多的蚕蛹图像细节结构特征。利用基于霍夫变换理论的形状匹配算法对处理前和处理后的400颗蚕蛹成像图像进行了雌雄识别试验研究，试验结果表明，相对于原始未处理的蚕蛹图像，经该文方法处理后的蚕蛹图像，雌雄蚕蛹识别率提高了40.5百分点。该文方法对西葫芦、南瓜等类非规则椭球体果蔬图像也能够进行有效的图像质量改善，这充分显示了该文方法的广泛适应性。

Silkworm pupa image restoration based on aliasing resolving algorithm and identifying male and female

Abstract: In the machine vision-based intelligent system for recognizing female or male silkworm pupa, much spatially-varying blur appears in silkworm pupa images and it could give rise to the loss of images textures and structures to a great extent due to the irregular ellipsoid shape of silkworm pupa and the limited depth of field of optical imaging system. This brings a challenge for an intelligent system to identify silkworm pupa''s gender. Shen''s method is supposed to be one of the state-of-the-art methods, but the PSF(point spread function) is estimated on a per pixel basis and parameter enumeration is required to meet the optimization criterion, which leads to a prohibitively large computation efforts. To solve this problem, we presented an effective method that the complicated restoration of spatially-varying blur silkworm pupa images was decomposed into the simple restoration of multiple images which have the same level blur and were part of original image. In this work, according to the variation of Tang''s method, the blur standard deviation at every pixel was estimated to construct a full defocus map of silkworm pupa image. The approximate blur standard deviations in defocus map were automatically sorted via the fuzzy C-means algorithm, and the original blur silkworm pupa image, based on this classification, was naturally segmented into several sub images possessing similar level blur. Then, each sub image was deblurred by using Lucy-Richardson (LR), and was merged to form a full silkworm pupa image. Eventually, Bilateral Filtering was used to eliminate the errors arising in the merging stage, and the high-quality silkworm pupa image was then obtained. To test this method, experiments (including both female and male silkworm pupa images) were conducted on the platform configured with CPU i5-2430 M, 2.4 GHz, memory 2 G, 32 bit operation system, matlab 2012 and VC++6.0. We set iteration steps of LR de-convolution as eight in real data experiments. Total variation Mean (TVM) was used to estimate the quality of the restored results. The experimental results showed that the performance of the proposed algorithm was better than Shen''s method. The method successfully removed spatially-varying blur and enhanced the image quality, which was verified in both qualitative (or visual) and quantitative ways. It can be seen that in real data experiments, our method effectively improved silkworm pupa images from which the spatially-varying blur was eliminated to a great extent, more image texture details were increased and sharpness contrast was much better. Meanwhile, in term of quantitation estimation, the TVM values of our method'' results were bigger than Shen''s results, which was further proof of our method''s good performance. It was noted that our method can also be conveniently extended to improve the quality of other vegetable images suffering from the spatially-varying blur, such as marrow and pumpkin, as shown in the experiments. After silkworm pupa image restoration, we achieved high accuracy of 92.3% in identifying male and female silkworm pupa. The proposed method can have a wide application of machine vision technologies.