Blurry image discrimination is a challenging and critical problem in computer vision. It is useful for image restoration, object recognition, and other image applications. In previous studies, researchers proposed a discrimination method based on hand-extracted features or deep learning. However, these methods are either pure data driven by deep learning or over-simplified assumptions on prior knowledge. As a result, a discrimination method is proposed for distinguishing sharp images and blurry

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... images based on a fusion network. The proposed method can automatically discriminate and detect blur without performing image restoration or blur kernel function estimation. Actually, the blur and the noise are extracted by the improved VGG16 network and texture noise extraction algorithm, respectively. Then the fusion network integrates the advantages of deep learning and hand-extracted features, and achieves ultimate highaccuracy discrimination results. Rigorous experiments performed on own dataset and other popular datasets with a number of blurry images and sharp images, including RealBlur dataset, BSD-B dataset, and GoPro dataset. The results show that the proposed method outperforms with an accuracy of 98% on our own dataset and 94.8% on the other dataset, which satisfies the requirements of the image applications. Similarly, we have compared our method with stateof-the-art methods to show its robustness and generalization ability. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.