New journal paper on synthetic image blurring for self-supervised deep blur detection

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Our paper “Self-Supervised Blur Detection from Synthetically Blurred Scenes” just got accepted for publication at the Image and Vision Computing journal (Q1).


The paper, shared work between Tecnalia and the Computer Vision Center/Universitat Autònoma de Barcelona, makes use of synthetic blurring to show how we can use self-supervised and weakly supervised learning techniques to train a Convolutional Neural Net on the task of segmenting the defocus or motion-blurred areas of an image without having access to images annotated under real blur.

More info and full text available here.