  Deep learning-based blind image deblurring plays an essential role in solving
image blur since all existing kernels are limited in modeling the real world
blur. Thus far, researchers focus on powerful models to handle the deblurring
problem and achieve decent results. For this work, in a new aspect, we discover
the great opportunity for image enhancement (e.g., deblurring) directly from
RAW images and investigate novel neural network structures benefiting RAW-based
learning. However, to the best of our knowledge, there is no available RAW
image deblurring dataset. Therefore, we built a new dataset containing both RAW
images and processed sRGB images and design a new model to utilize the unique
characteristics of RAW images. The proposed deblurring model, trained solely
from RAW images, achieves the state-of-art performance and outweighs those
trained on processed sRGB images. Furthermore, with fine-tuning, the proposed
model, trained on our new dataset, can generalize to other sensors.
Additionally, by a series of experiments, we demonstrate that existing
deblurring models can also be improved by training on the RAW images in our new
dataset. Ultimately, we show a new venue for further opportunities based on the
devised novel raw-based deblurring method and the brand-new Deblur-RAW dataset.
