Conditional deep learning approach for the Helsinki deblur challenge 2021
Ji Li, Weixi Wang · Inverse Problems and Imaging · 2022
Image captured by an optical lens-based imaging device usually has some part that appears out of focus and blurry. Such degradation is known as defocus blur. It happens when the object being captured is too far away from (or close to) the camera lens. Blurring removal is also helpful for other down-stream tasks, such as optical character recognition (OCR). Nowadays the deep learning is a prominent solution to practical image deblurring using supervised learning from a paired dataset. The Helsinki Deblur Challenge 2021 (HDC2021) provided the paired images taken by two identical cameras of the same target but with different conditions. The paper details the 2nd place solution to the challenge with discussion. Although the solution was designed to participate with the challenge, the two-stage framework and the way of incorporating the blur level information into the network architecture can be applied to other image restoration problems.