The work aims to research the deblurring methods in digital image processing that restore clear images from damaged blurred ones. A two-stage self-adaptive deblurring algorithm based on deconvolution and convolutional neural network in Y channel (SDYCNN) was proposed, with respect to the problem that the existing deblurring algorithm could not preserve the high frequency information of the images and was likely to result in ringing effect. In the first stage, the digital images were transferred to YUV color space. According to the correspondence relationship between the image with no reference quality assessment scores and the blur kernel size, the blur kernel size was adaptively determined and the deconvolution enhancement was done in Y channel. In the second stage, the deconvolution enhancement in the first stage was taken as the preprocessing method. The mapping relationship between the deconvolution-enhanced images and ground truth images was established by 4-layer convolutional neural network and could be used for image deblurring. The first stage was sufficient to achieve satisfactory results for slightly blurred image. The severely blurred images after the deconvolution enhancement in the first stage were also helpful for the rapid feature extraction in neural network. Experiment results demonstrate that the proposed algorithm can not only properly restore the blurred images, but also significantly improve the compute efficiency.