Blur Synthesis

Blur synthesis focuses on generating realistic blurred images, primarily to improve training data for image deblurring and related tasks like video super-resolution and image compression. Current research emphasizes creating more realistic blur simulations by modeling the camera's imaging process, including factors like motion blur and depth of field, often employing neural networks, particularly variations of UNets and transformers, to achieve this. These advancements are crucial for enhancing the performance of deep learning models in various computer vision applications, leading to higher-quality image and video processing in areas such as medical imaging and mobile photography.