Search Results for author: Pablo Navarrete Michelini

Found 14 papers, 8 papers with code

edge-SR: Super-Resolution For The Masses

1 code implementation23 Aug 2021 Pablo Navarrete Michelini, Yunhua Lu, Xingqun Jiang

We explore possible solutions to this problem with the aim to fill the gap between classic upscalers and small deep learning configurations.

Image Super-Resolution

Back-Projection Pipeline

no code implementations25 Jan 2021 Pablo Navarrete Michelini, Hanwen Liu, Yunhua Lu, Xingqun Jiang

We propose a simple extension of residual networks that works simultaneously in multiple resolutions.

Rain Removal Super-Resolution

Multi-Grid Back-Projection Networks

no code implementations1 Jan 2021 Pablo Navarrete Michelini, Wenbin Chen, Hanwen Liu, Dan Zhu, Xingqun Jiang

For this target we propose a strategy using noise inputs in different resolution scales to control the amount of artificial details generated in the output.

MGBPv2: Scaling Up Multi-Grid Back-Projection Networks

1 code implementation27 Sep 2019 Pablo Navarrete Michelini, Wenbin Chen, Hanwen Liu, Dan Zhu

Here, we describe our solution for the AIM-2019 Extreme Super-Resolution Challenge, where we won the 1st place in terms of perceptual quality (MOS) similar to the ground truth and achieved the 5th place in terms of high-fidelity (PSNR).

Image and Video Processing

Multi-Scale Recursive and Perception-Distortion Controllable Image Super-Resolution

1 code implementation27 Sep 2018 Pablo Navarrete Michelini, Dan Zhu, Hanwen Liu

We describe our solution for the PIRM Super-Resolution Challenge 2018 where we achieved the 2nd best perceptual quality for average RMSE<=16, 5th best for RMSE<=12. 5, and 7th best for RMSE<=11. 5.

Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Signal Processing

Multigrid Backprojection Super-Resolution and Deep Filter Visualization

1 code implementation25 Sep 2018 Pablo Navarrete Michelini, Hanwen Liu, Dan Zhu

It is also residual since we use the network to update the outputs of a classic upscaler.


Convolutional Networks with MuxOut Layers as Multi-rate Systems for Image Upscaling

no code implementations22 May 2017 Pablo Navarrete Michelini, Hanwen Liu

We interpret convolutional networks as adaptive filters and combine them with so-called MuxOut layers to efficiently upscale low resolution images.

Cannot find the paper you are looking for? You can Submit a new open access paper.