Abstract
During the image acquisition process, some level of noise is usually added to the data mainly due to physical limitations of the sensor, and also regarding imprecisions during the data transmission and manipulation. Therefore, the resultant image needs to be further processed for noise attenuation without losing details. In this work, we attempt to denoise images using the advantage of sparse-based encoding and deep networks. Experiments on public images corrupted by different levels of Gaussian noise support the effectiveness of the proposed approach concerning some state-of-the-art image denoising approaches.
The authors are grateful to Capes, CNPq grant #306166/2014-3, and FAPESP grants #2014/16250-9, #2014/12236-1, 2016/19403-6, and #2018/21934-5.
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Notes
- 1.
Pictures of objects belonging to 101 categories, and about 40 to 800 images per category.
- 2.
The normalization consists in subtracting all pixel values by 0.5 and dividing them by 0.2.
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Pires, R.G., Santos, D.S., Souza, G.B., Levada, A.L.M., Papa, J.P. (2019). A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_46
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