Collaborative Blind Image Deblurring
This work addresses blind image deblurring for computer vision applications, offering incremental improvements over existing neural network methods.
The paper tackled the problem of blind image deblurring by proposing a collaborative scheme that jointly processes patches with similar blur, implemented in a neural architecture with a pooling layer. The approach achieved significant quantitative and qualitative improvements on synthetic and real-world benchmarks for tasks like image sharpening, camera shake removal, and optical aberration correction.
Blurry images usually exhibit similar blur at various locations across the image domain, a property barely captured in nowadays blind deblurring neural networks. We show that when extracting patches of similar underlying blur is possible, jointly processing the stack of patches yields superior accuracy than handling them separately. Our collaborative scheme is implemented in a neural architecture with a pooling layer on the stack dimension. We present three practical patch extraction strategies for image sharpening, camera shake removal and optical aberration correction, and validate the proposed approach on both synthetic and real-world benchmarks. For each blur instance, the proposed collaborative strategy yields significant quantitative and qualitative improvements.