# Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations

arXiv: https://arxiv.org/abs/2004.06965 · HTML: https://mlsanity.com/paper/2004.06965/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 30 of 61)

- [Unsupervised Degradation Representation Learning for Blind Super-Resolution](https://mlsanity.com/paper/2104.00416/) (2021)
- [TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up](https://mlsanity.com/paper/2102.07074/) (2021)
- [Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution](https://mlsanity.com/paper/2203.14216/) (2022)
- [Blind Image Super-Resolution: A Survey and Beyond](https://mlsanity.com/paper/2107.03055/) (2021)
- [Flow-based Kernel Prior with Application to Blind Super-Resolution](https://mlsanity.com/paper/2103.15977/) (2021)
- [Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution](https://mlsanity.com/paper/2108.05302/) (2021)
- [Deep Constrained Least Squares for Blind Image Super-Resolution](https://mlsanity.com/paper/2202.07508/) (2022)
- [Learning to Deblur using Light Field Generated and Real Defocus Images](https://mlsanity.com/paper/2204.00367/) (2022)
- [Blind Image Super-Resolution via Contrastive Representation Learning](https://mlsanity.com/paper/2107.00708/) (2021)
- [MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking](https://mlsanity.com/paper/2107.10433/) (2021)
- [Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network](https://mlsanity.com/paper/2104.09556/) (2021)
- [Knowledge Distillation based Degradation Estimation for Blind Super-Resolution](https://mlsanity.com/paper/2211.16928/) (2022)
- [COMISR: Compression-Informed Video Super-Resolution](https://mlsanity.com/paper/2105.01237/) (2021)
- [Variational Deep Image Restoration](https://mlsanity.com/paper/2207.01074/) (2022)
- [A Systematic Survey of Deep Learning-based Single-Image Super-Resolution](https://mlsanity.com/paper/2109.14335/) (2021)
- [KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution](https://mlsanity.com/paper/2209.10305/) (2022)
- [A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution](https://mlsanity.com/paper/2404.15620/) (2024)
- [Toward Real-World Super-Resolution via Adaptive Downsampling Models](https://mlsanity.com/paper/2109.03444/) (2021)
- [Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal](https://mlsanity.com/paper/2306.09008/) (2023)
- [Adaptive Convolutions with Per-pixel Dynamic Filter Atom](https://mlsanity.com/paper/2108.07895/) (2021)
- [Meta-Learning based Degradation Representation for Blind Super-Resolution](https://mlsanity.com/paper/2207.13963/) (2022)
- [Real-World Light Field Image Super-Resolution via Degradation Modulation](https://mlsanity.com/paper/2206.06214/) (2022)
- [Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution](https://mlsanity.com/paper/2208.13436/) (2022)
- [AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions](https://mlsanity.com/paper/2305.03387/) (2023)
- [Region-Adaptive Transform with Segmentation Prior for Image Compression](https://mlsanity.com/paper/2403.00628/) (2024)
- [CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution](https://mlsanity.com/paper/2405.07648/) (2024)
- [Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations](https://mlsanity.com/paper/2104.03926/) (2021)
- [Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution](https://mlsanity.com/paper/2110.12151/) (2021)
- [TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing](https://mlsanity.com/paper/2203.10489/) (2022)
- [Degradation-Guided Meta-Restoration Network for Blind Super-Resolution](https://mlsanity.com/paper/2207.00943/) (2022)

## Abstract (arXiv)

Deep Convolutional Neural Networks (CNNs) have achieved remarkable results on Single Image Super-Resolution (SISR). Despite considering only a single degradation, recent studies also include multiple degrading effects to better reflect real-world cases. However, most of the works assume a fixed combination of degrading effects, or even train an individual network for different combinations. Instead, a more practical approach is to train a single network for wide-ranging and variational degradations. To fulfill this requirement, this paper proposes a unified network to accommodate the variations from inter-image (cross-image variations) and intra-image (spatial variations). Different from the existing works, we incorporate dynamic convolution which is a far more flexible alternative to handle different variations. In SISR with non-blind setting, our Unified Dynamic Convolutional Network for Variational Degradations (UDVD) is evaluated on both synthetic and real images with an extensive set of variations. The qualitative results demonstrate the effectiveness of UDVD over various existing works. Extensive experiments show that our UDVD achieves favorable or comparable performance on both synthetic and real images.