Helsinki Deblur Challenge

The Helsinki Deblur Challenge spurred significant advancements in image deblurring, focusing on recovering sharp images from blurry inputs, particularly in challenging scenarios like text recognition and microscopy. Current research emphasizes innovative training strategies, such as blur-to-blur conversion and multi-output architectures, alongside the integration of auxiliary data sources like polarization information and event cameras to improve deblurring accuracy. These improvements have implications for various applications, including medical imaging, computer vision tasks (like optical character recognition), and enhancing the quality of images captured under difficult conditions.