  Non-blind deblurring methods achieve decent performance under the accurate
blur kernel assumption. Since the kernel uncertainty (i.e. kernel error) is
inevitable in practice, semi-blind deblurring is suggested to handle it by
introducing the prior of the kernel (or induced) error. However, how to design
a suitable prior for the kernel (or induced) error remains challenging.
Hand-crafted prior, incorporating domain knowledge, generally performs well but
may lead to poor performance when kernel (or induced) error is complex.
Data-driven prior, which excessively depends on the diversity and abundance of
training data, is vulnerable to out-of-distribution blurs and images. To
address this challenge, we suggest a dataset-free deep residual prior for the
kernel induced error (termed as residual) expressed by a customized untrained
deep neural network, which allows us to flexibly adapt to different blurs and
images in real scenarios. By organically integrating the respective strengths
of deep priors and hand-crafted priors, we propose an unsupervised semi-blind
deblurring model which recovers the latent image from the blurry image and
inaccurate blur kernel. To tackle the formulated model, an efficient
alternating minimization algorithm is developed. Extensive experiments
demonstrate the favorable performance of the proposed method as compared to
data-driven and model-driven methods in terms of image quality and the
robustness to the kernel error.
