We propose a novel method based on Convolutional Neural Networks for content-preserving tone adjustment. The method is at the same time fast and accurate since we decouple the inference of the parameters and the color transform: the parameters are inferred from a downsampled version of the input image and the transformation is applied to the full resolution input. The method includes two steps of image enhancement: the first one is a global color transformation, while the second one is a local transformation. Experiments conducted on the DPED - DSLR Photo Enhancement Dataset, that has been used for the NTIRE19 Image Enhancement Challenge, and on the MIT-Adobe FiveK dataset, that is widely used for image enhancement, demonstrate the effectiveness of the proposed method.
Content-preserving tone adjustment for image enhancement / Bianco, S., Cusano, C., Piccoli, F., Schettini, R.. - 2019:(2019), pp. 1936-1943. (32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 usa 2019) [10.1109/CVPRW.2019.00245].
Content-preserving tone adjustment for image enhancement
Piccoli, F;
2019-01-01
Abstract
We propose a novel method based on Convolutional Neural Networks for content-preserving tone adjustment. The method is at the same time fast and accurate since we decouple the inference of the parameters and the color transform: the parameters are inferred from a downsampled version of the input image and the transformation is applied to the full resolution input. The method includes two steps of image enhancement: the first one is a global color transformation, while the second one is a local transformation. Experiments conducted on the DPED - DSLR Photo Enhancement Dataset, that has been used for the NTIRE19 Image Enhancement Challenge, and on the MIT-Adobe FiveK dataset, that is widely used for image enhancement, demonstrate the effectiveness of the proposed method.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
