Abstract
Camera motion introduces motion blur, affecting many
computer vision tasks. Dark Channel Prior (DCP) helps the
blind deblurring on scenes including natural, face, text, and
low-illumination images. However, it has limitations and is
less likely to support the kernel estimation while bright pixels dominate the input image. We observe that the bright
pixels in the clear images are not likely to be bright after the
blur process. Based on this observation, we first illustrate
this phenomenon mathematically and define it as the Bright
Channel Prior (BCP). Then, we propose a technique for deblurring such images which elevates the performance of existing motion deblurring algorithms. The proposed method
takes advantage of both Bright and Dark Channel Prior.
This joint prior is named as extreme channels prior and
is crucial for achieving efficient restorations by leveraging
both the bright and dark information. Extensive experimental results demonstrate that the proposed method is more
robust and performs favorably against the state-of-the-art
image deblurring methods on both synthesized and natural
images