Performing Defocus Deblurring by Modeling its Formation Process

Zhengbo Zhang, Lin Geng Foo, Hossein Rahmani, Jun Liu, De Wen Soh; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 5791-5801

Single image defocus deblurring (SIDD) is a challenging task that aims to recover an all-in-focus image from a defocused one. In this paper, we make the observation that a defocused image can be viewed as a blend of illuminated blobs based on fundamental imaging principles, and the defocus blur in the defocused image is caused by large illuminated blobs intermingling with each other. Thus, from a novel perspective, we perform SIDD by adjusting the shape and opacity of the illuminated blobs that compose the defocused image. With this aim, we adopt a novel 2D Gaussian blob representation for illuminated blobs and a differentiable rasterization method to obtain the parameters of the 2D Gaussian blobs that compose the defocused image. Additionally, we propose a blob deblurrer to adjust the parameters of the 2D Gaussian blobs corresponding to the defocused image, thereby obtaining a sharp image. We also explore incorporating prior depth information via our depth-based regularization loss to regularize the size of Gaussian blobs, further improving the performance of our method. Extensive experiments on five widely-used datasets validate the effectiveness of our proposed method.