# Efficient Residual Dense Block Search for Image Super-Resolution

arXiv: https://arxiv.org/abs/1909.11409 · HTML: https://mlsanity.com/paper/1909.11409/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 30 of 37)

- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution](https://mlsanity.com/paper/2302.13800/) (2023)
- [ShuffleMixer: An Efficient ConvNet for Image Super-Resolution](https://mlsanity.com/paper/2205.15175/) (2022)
- [Blueprint Separable Residual Network for Efficient Image Super-Resolution](https://mlsanity.com/paper/2205.05996/) (2022)
- [NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results](https://mlsanity.com/paper/2205.05675/) (2022)
- [Learning Frequency-aware Dynamic Network for Efficient Super-Resolution](https://mlsanity.com/paper/2103.08357/) (2021)
- [Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search](https://mlsanity.com/paper/2108.08910/) (2021)
- [Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances](https://mlsanity.com/paper/2209.13131/) (2022)
- [Five A+ Network: You Only Need 9K Parameters for Underwater Image Enhancement](https://mlsanity.com/paper/2305.08824/) (2023)
- [A Two-Stage Attentive Network for Single Image Super-Resolution](https://mlsanity.com/paper/2104.10488/) (2021)
- [Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution](https://mlsanity.com/paper/2207.12577/) (2022)
- [Collapsible Linear Blocks for Super-Efficient Super Resolution](https://mlsanity.com/paper/2103.09404/) (2021)
- [Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey](https://mlsanity.com/paper/2404.16484/) (2024)
- [Edge-enhanced Feature Distillation Network for Efficient Super-Resolution](https://mlsanity.com/paper/2204.08759/) (2022)
- [Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report](https://mlsanity.com/paper/2105.08629/) (2021)
- [Attentive Fine-Grained Structured Sparsity for Image Restoration](https://mlsanity.com/paper/2204.12266/) (2022)
- [Online Video Super-Resolution with Convolutional Kernel Bypass Graft](https://mlsanity.com/paper/2208.02470/) (2022)
- [HIPA: Hierarchical Patch Transformer for Single Image Super Resolution](https://mlsanity.com/paper/2203.10247/) (2022)
- [Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions](https://mlsanity.com/paper/2106.03727/) (2021)
- [GhostSR: Learning Ghost Features for Efficient Image Super-Resolution](https://mlsanity.com/paper/2101.08525/) (2021)
- [D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution](https://mlsanity.com/paper/2103.14373/) (2021)
- [3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification](https://mlsanity.com/paper/2101.04287/) (2021)
- [Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution](https://mlsanity.com/paper/2105.03939/) (2021)
- [Overcoming Distribution Mismatch in Quantizing Image Super-Resolution Networks](https://mlsanity.com/paper/2307.13337/) (2023)
- [Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution](https://mlsanity.com/paper/2404.02573/) (2024)
- [Coarse-to-Fine Searching for Efficient Generative Adversarial Networks](https://mlsanity.com/paper/2104.09223/) (2021)
- [IMDeception: Grouped Information Distilling Super-Resolution Network](https://mlsanity.com/paper/2204.11463/) (2022)
- [Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks](https://mlsanity.com/paper/2304.10749/) (2023)
- [Automated Deepfake Detection](https://mlsanity.com/paper/2106.10705/) (2021)
- [Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2110.11084/) (2021)

## Abstract (arXiv)

Although remarkable progress has been made on single image super-resolution due to the revival of deep convolutional neural networks, deep learning methods are confronted with the challenges of computation and memory consumption in practice, especially for mobile devices. Focusing on this issue, we propose an efficient residual dense block search algorithm with multiple objectives to hunt for fast, lightweight and accurate networks for image super-resolution. Firstly, to accelerate super-resolution network, we exploit the variation of feature scale adequately with the proposed efficient residual dense blocks. In the proposed evolutionary algorithm, the locations of pooling and upsampling operator are searched automatically. Secondly, network architecture is evolved with the guidance of block credits to acquire accurate super-resolution network. The block credit reflects the effect of current block and is earned during model evaluation process. It guides the evolution by weighing the sampling probability of mutation to favor admirable blocks. Extensive experimental results demonstrate the effectiveness of the proposed searching method and the found efficient super-resolution models achieve better performance than the state-of-the-art methods with limited number of parameters and FLOPs.