Image Deblurring Using a Robust Loss Function

Deblurring images with outliers has always been a significantly challenging problem. Previous methods mainly involved complex operations, such as outlier and light streak detection, or sophisticated image priors for blur-kernel estimation, which increased the difficulty of deblurring images. Therefore, we developed a simple, yet efficient, blind deblurring algorithm in this study for handling images with outliers. To eliminate the impact of outliers during the kernel estimation process, we employed a robust Welsch loss function to characterize the data-fidelity term of our model. We observed that this function could extract significant edges successfully. Therefore, the image regularization term was also described by the same function. Using this unified robust function to describe our model can considerably reduce the complexity of the algorithm. Moreover, we derived a flexible weight function from the Welsch function to further improve the efficiency of our algorithm. To finally obtain accurate latent images, we developed a robust non-blind deblurring approach based on this flexible weight function. The experimental results indicate that our approach outperforms state-of-the-art methods in deblurring images with or without outliers. Compared with the method specifically for outliers, the recovery performance of our method can be improved by 12.9% (considering a dataset with impulse noise), and the execution efficiency is about 1.5 times faster.