Radon transform-based motion blurred silkworm pupa image restoration

As for machine vision-based intelligent system in the application of discriminating and sorting the sex of silkworm pupae, the tail gonad was the unique physiological feature. However, motion blur, resulting from the live silkworm pupa’s writhing motion at the moment of capturing image, could lose textures and structures (such as edge and tail gonad etc.) dramatically, which casted great challenges for sex identification. To increase the image quality and relieve the difficulty of discrimination caused by motion blur, an effective approach that including three stages was proposed in this work. In the image prediction stage, first sharp edges were acquired by using filtering techniques. Then the initial blur kernel was computed with Gaussian prior. The coarse version latent image was deconvoluted in the Fourier domain. In the kernel refinement stage, the Radon transform was applied to estimate the accurate kernel. In the final restoration step, a TV-L1 deconvolution model was carried out to obtain a better result. The experimental results showed that benefiting from the prediction step and kernel refinement step, the kernel was more accurate and the recovered image contained much more textures. It revealed that the proposed method was useful in removing the motion blur. Furthermore, the method could also be applied to other fields.