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CNN-BASED LUMINANCE AND COLOR CORRECTION FOR ILL-EXPOSED IMAGES

Citation Author(s):
Cristiano Rafael Steffens, Valquiria Huttner, Lucas Ricardo Vieira Messias, Paulo Lilles Jorge Drews-Jr, Silvia Silva da Costa Botelho, Rodrigo da Silva Guerra
Submitted by:
Cristiano Steffens
Last updated:
20 September 2019 - 8:43pm
Document Type:
Presentation Slides
Document Year:
2019
Event:
Presenters:
Rodrigo da Silva Guerra​
Paper Code:
1412

Abstract

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.

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