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Found 4 papers, 1 papers with code
Improving Stability during Upsampling -- on the Importance of Spatial Context
While during downsampling, aliases and artifacts can be reduced by blurring feature maps, the emergence of fine details is crucial during upsampling.
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix
We consider the recently proposed Restormer model, as well as NAFNet and the "Baseline network" which are both simplified versions of a Restormer.
Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters
The success of deep learning is frequently described as the ability to train all parameters of a network on a specific application in an end-to-end fashion.
CosPGD: a unified white-box adversarial attack for pixel-wise prediction tasks
Further, we set a new benchmark for adversarial attacks on optical flow, and image restoration displaying the ability to extend to any pixel-wise prediction task.
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