Abstract: 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.