CNN-Based Luminance And Color Correction For ILL-Exposed Images

Image restoration and image enhancement are critical image processing tasks since good image quality is mandatory for many image applications. We are particularly interested in the restoration of ill-exposed images. These effects are caused by sensor limitation or optical arrangement. They prevent t...

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Bibliographic Details
Published in2019 IEEE International Conference on Image Processing (ICIP) pp. 3252 - 3256
Main Authors Steffens, C. R., Huttner, V., Messias, L. R. V., Drews, P. L. J., Botelho, S. S. C., Guerra, R. S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2019
Subjects
Cameras
Clipping
Convolutional codes
Image coding
Image color analysis
Image enhancement
Image quality
Image reconstruction
Image restoration
Neural networks
Saturation
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Summary:Image restoration and image enhancement are critical image processing tasks since good image quality is mandatory for many image applications. We are particularly interested in the restoration of ill-exposed images. These effects are caused by sensor limitation or optical arrangement. They prevent the details of the scene from being adequately represented in the captured image. We proposed a deep neural network model due to the number of uncontrolled variables that impact the acquisition. The proposed network can converge in a representative model from the training data, loss, optimization and activation functions. The obtained results are evaluated using several image quality index which indicate that the proposed network is able to improve images damaged by heterogeneous exposure. Furthermore, our method offers a significant gain over the state-of-the-art methods both in simulated data and real data.
ISSN:2381-8549
DOI:10.1109/ICIP.2019.8803546