13.1: Invited Paper: Multigrid Backprojection Super‐Resolution and Deep Filter Visualization

@article{Michelini2018131IP,
  title={13.1: Invited Paper: Multigrid Backprojection Super‐Resolution and Deep Filter Visualization},
  author={Pablo Navarrete Michelini and Hanwen Liu and Dan Zhu},
  journal={SID Symposium Digest of Technical Papers},
  year={2018},
  volume={50}
}
We introduce a novel deep‐learning architecture for image upscaling by large factors (e.g. 4x, 8x) based on examples of pristine high‐resolution images. Our target is to reconstruct high‐resolution images from their downscale versions. The proposed system performs a multi‐level progressive upscaling, starting from small factors (2x) and updating for higher factors (4x and 8x). The system is recursive as it repeats the same procedure at each level. It is also residual since we use the network to… 
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20 Citations
Highly Influential Citations
1
Background Citations
6
Methods Citations
13
Results Citations
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20 Citations

Multi–Grid Back–Projection Networks

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.

MGBPv2: Scaling Up Multi-Grid Back-Projection Networks

The second generation of MultiGrid BackProjection networks (MGBPv2) is introduced whose major modifications make the system scalable and more general than its predecessor, and which can balance between high quality and high performance.

Back–Projection Pipeline

This work proposes a novel solution to make back-projections run in multiple resolutions by using a data pipeline workflow and is represented by a system of ODEs, as opposed to a single ODE in the case of ResNets.

D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution

D2C-SR is presented, a novel framework for the task of real-world image super-resolution with better accuracy and visual improvements against state-of-the-art methods, with a significantly less parameters number and the D2C structure can be applied as a generalized structure to some other methods to obtain improvement.

DIV8K: DIVerse 8K Resolution Image Dataset

The DIVerse 8K resolution image dataset (DIV8K) is introduced, which contains a over 1500 images with a resolution up to 8K, and is therefore the ideal dataset for training and benchmarking super-resolution approaches, applicable to extreme upscaling factors of 32x and beyond.

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

A discriminator for adversarial training with the following novel properties is proposed: it is multi–scale that resembles a progressive–GAN; it is recursive that balances the architecture of the generator; and it includes a new layer to capture significant statistics of natural images.

Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks.

Experimental results and ablation studies show that the proposed algorithm can rival state-of-the-art SR algorithms, both perceptually (PI-minimization) and objectively (PSNR-maximizing) with fewer parameters.

Inverse-Based Approach to Explaining and Visualizing Convolutional Neural Networks

A new inverse-based approach that computes the inverse of a feedforward pass to identify activations of interest in lower layers and develops a novel plot that shows the tradeoff between the amount of activations and the rate of class reidentification.

AIM 2019 Challenge on Image Extreme Super-Resolution: Methods and Results

This paper reviews the AIM 2019 challenge on extreme image super-resolution, the problem of restoring of rich details in a low resolution image, and gauges the experimental protocol and baselines for the extreme image Superresolution task.

Image super-resolution using progressive residual multi-dilated aggregation network

A progressive residual multi-dilated aggregation network (PRMAN), which performs multi-level upsampling to reconstruct images with large-scale factors and exceeds the state-of-the-art methods in most cases.

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