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We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics.
Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality.
Ranked #8 on Image Super-Resolution on Set14 - 4x upscaling
In this paper, we propose a predict-refine architecture, BASNet, and a new hybrid loss for Boundary-Aware Salient object detection.
Ranked #1 on Salient Object Detection on DUTS-TE
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.
In this paper, we propose a structure-preserving super resolution method to alleviate the above issue while maintaining the merits of GAN-based methods to generate perceptual-pleasant details.
Ranked #34 on Image Super-Resolution on Urban100 - 4x upscaling
The small CNN works on the down-sampled version of the input image to predict content-dependent weights to fuse the multiple basis 3D LUTs into an image-adaptive one, which is employed to transform the color and tone of source images efficiently.
Our loss function includes two perceptual losses: a feature loss from a visual perception network, and an adversarial loss that encodes characteristics of images in the transmission layers.