The visual motion blur elimination method for silicon nitride bearing roller fissures based on U-Net asymmetric multi-scale feature fusion

The visual motion blur imaging for the feature recognition process of silicon nitride bearing roller fissures is a pathological problem. This is solved by proposing squeeze-and-excitation asymmetric fusion of multi-scale features with high-frequency loss attention coupled U-Net (MHU-Net). The visual motion blur elimination of fissure features on silicon nitride bearing rollers is achieved. In the deblurring model, the multi-scale feature information on silicon nitride bearing roller fissures is blocked and there is weak correlation between channels. A design for an asymmetric fusion multi-scale feature module under the channel information compression–excitation mode is proposed. It successfully balances the channel information from different scales while integrating multi-scale features in image fusion. The high-frequency region of fissure features on silicon nitride bearing rollers is analyzed. Around the high-frequency feature loss in the multi-frequency domain of images combined with spatial feature loss, a multi-frequency band high-frequency loss attention module is built. Then, the complete structural details of silicon nitride bearing roller fissures are obtained. The proposed algorithm achieves a peak signal-to-noise ratio of 27.58 and a structural similarity of 0.847 on our self-made silicon nitride defect motion dataset. The visual motion blur of fissure features is noticeably eliminated. The restored image exhibits complete details in the feature structures and overall region smoothness.