Abstract: Label image sharpness is the key point that influences the effectiveness of visual inspection during the label qualify inspection of household appliances with machine vision． To solve the motion blurring problem of tag image captured by camera fixed on the moving robot，a regularization model based on L0 norm was proposed． The general methods of deblurring algorithms have considered and implemented much of image priors for blurred images，which can obtain the well-done visual overall effect． However，these general algorithms does not take the characteristic of identification tag into consideration，and this is an important prior to deblurring． To this point，a specific model consisting of image gradient prior regularization and sparse regularization was proposed to analyze pixel distribution and gradient distribution features of label image． The experimental results show that，as compared with other deblurring algorithms，the proposed method can effectively suppress the ringing effect at the edge of the label image while restoring label image sharpness on synthetic images and real images，and the real computation speed is increased by 80. 52% ．

Key words: label image, motion blur, regularization model, sparse gradient prior