mlsanityData to 7 Oct 2026

Multi-Grid Back-Projection Networks

arXiv 2101.00150 ↗First posted 1 Jan 2021eess.IV cs.CV cs.LGcited by 1

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Abstract (arXiv)

Multi-Grid Back-Projection (MGBP) is a fully-convolutional network architecture that can learn to restore images and videos with upscaling artifacts. Using the same strategy of multi-grid partial differential equation (PDE) solvers this multiscale architecture scales computational complexity efficiently with increasing output resolutions. The basic processing block is inspired in the iterative back-projection (IBP) algorithm and constitutes a type of cross-scale residual block with feedback from low resolution references. The architecture performs in par with state-of-the-arts alternatives for regression targets that aim to recover an exact copy of a high resolution image or video from which only a downscale image is known. A perceptual quality target aims to create more realistic outputs by introducing artificial changes that can be different from a high resolution original content as long as they are consistent with the low resolution input. 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. The noise input controls the amount of innovation that the network uses to create artificial realistic details. The effectiveness of this strategy is shown in benchmarks and it is explained as a particular strategy to traverse the perception-distortion plane.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 39 of its 86 references that are in the map. First posted on arXiv 1 Jan 2021.

Builds on

39 of 86 references.

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Citing papers per year: 2021: 1.

1 paper.

  1. 2021NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Resultscited by 26