Automatic image cracks detection and removal on mobile devices

V. Voronin; V. Marchuk; R. Sizyakin; N. Gapon; M. Pismenskova; Sergei Makov; S. Tokareva

doi:10.1117/12.2224339

19 May 2016 Automatic image cracks detection and removal on mobile devices

V. Voronin, V. Marchuk, R. Sizyakin, N. Gapon, M. Pismenskova, Sergei Makov, S. Tokareva

Proceedings Volume 9869, Mobile Multimedia/Image Processing, Security, and Applications 2016; 98690R (2016) https://doi.org/10.1117/12.2224339
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

Abstract

Some of old photographs are damaged due to improper archiving (e.g. affected by direct sunlight, humidity, insects, etc.) or have a physical damage resulting on appearance of cracks, scratches on photographs, non-necessary signs, spots, dust, and so on. This paper focuses on detection and removal of cracks from digital images. The proposed method consists of the following steps: pre-processing, crack detection and image reconstruction. A pre-processing step is used to suppress a noise and small defects in images. For a crack identification we use modified local binary patterns to form a feature vectors, and a non-linear SVM for a crack recognition. The combined inpainting method using structure and texture restoration is applied at the image reconstruction step. Image inpainting is the process of restoring the lost or damaged regions or modifying the image contents imperceptibly. This technique detects and removes the horizontal, vertical, diagonal cracks and other defects on complex scenes of image. We implemented proposed method on some mobile platforms for automatic image enhancement. Presented examples demonstrate the effectiveness of the algorithm in cracks detection and removal.

Citation Download Citation

V. Voronin, V. Marchuk, R. Sizyakin, N. Gapon, M. Pismenskova, Sergei Makov, and S. Tokareva "Automatic image cracks detection and removal on mobile devices", Proc. SPIE 9869, Mobile Multimedia/Image Processing, Security, and Applications 2016, 98690R (19 May 2016); https://doi.org/10.1117/12.2224339

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