# Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution

arXiv: https://arxiv.org/abs/1809.11130 · HTML: https://mlsanity.com/paper/1809.11130/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2018.

## Built on by (most cited first, 30 of 42)

- [HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution](https://mlsanity.com/paper/2407.05878/) (2024)
- [Student Network Learning via Evolutionary Knowledge Distillation](https://mlsanity.com/paper/2103.13811/) (2021)
- [Boosting Neural Representations for Videos with a Conditional Decoder](https://mlsanity.com/paper/2402.18152/) (2024)
- [DVMSR: Distillated Vision Mamba for Efficient Super-Resolution](https://mlsanity.com/paper/2405.03008/) (2024)
- [NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results](https://mlsanity.com/paper/2106.03839/) (2021)
- [Attention in Attention Network for Image Super-Resolution](https://mlsanity.com/paper/2104.09497/) (2021)
- [A Two-Stage Attentive Network for Single Image Super-Resolution](https://mlsanity.com/paper/2104.10488/) (2021)
- [ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution](https://mlsanity.com/paper/2307.00781/) (2023)
- [Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2207.02796/) (2022)
- [CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2503.06896/) (2025)
- [Dual-Stage Approach Toward Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2204.04387/) (2022)
- [A heterogeneous group CNN for image super-resolution](https://mlsanity.com/paper/2209.12406/) (2022)
- [Learning from Multi-Perception Features for Real-Word Image Super-resolution](https://mlsanity.com/paper/2305.18547/) (2023)
- [Criteria Comparative Learning for Real-scene Image Super-Resolution](https://mlsanity.com/paper/2207.12767/) (2022)
- [Dense Dual-Attention Network for Light Field Image Super-Resolution](https://mlsanity.com/paper/2110.12114/) (2021)
- [Image Super-resolution with An Enhanced Group Convolutional Neural Network](https://mlsanity.com/paper/2205.14548/) (2022)
- [Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution](https://mlsanity.com/paper/2105.03939/) (2021)
- [Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution](https://mlsanity.com/paper/2304.04589/) (2023)
- [Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution](https://mlsanity.com/paper/2103.15290/) (2021)
- [Single image super-resolution based on trainable feature matching attention network](https://mlsanity.com/paper/2405.18872/) (2024)
- [Feedback Pyramid Attention Networks for Single Image Super-Resolution](https://mlsanity.com/paper/2106.06966/) (2021)
- [Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmark](https://mlsanity.com/paper/2207.03367/) (2022)
- [GRAN: Ghost Residual Attention Network for Single Image Super Resolution](https://mlsanity.com/paper/2302.14557/) (2023)
- [Super-Resolving Face Image by Facial Parsing Information](https://mlsanity.com/paper/2304.02923/) (2023)
- [RealOSR: Latent Guidance Boosts Diffusion-based Real-world Omnidirectional Image Super-Resolutions](https://mlsanity.com/paper/2412.09646/) (2024)
- [Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal Surveillance](https://mlsanity.com/paper/2111.06830/) (2021)
- [Global Priors Guided Modulation Network for Joint Super-Resolution and Inverse Tone-Mapping](https://mlsanity.com/paper/2208.06885/) (2022)
- [Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment](https://mlsanity.com/paper/2509.06442/) (2025)
- [A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective](https://mlsanity.com/paper/2111.13078/) (2021)
- [Improved Super Resolution of MR Images Using CNNs and Vision Transformers](https://mlsanity.com/paper/2207.11748/) (2022)

## Abstract (arXiv)

The performance of single image super-resolution has achieved significant improvement by utilizing deep convolutional neural networks (CNNs). The features in deep CNN contain different types of information which make different contributions to image reconstruction. However, most CNN-based models lack discriminative ability for different types of information and deal with them equally, which results in the representational capacity of the models being limited. On the other hand, as the depth of neural networks grows, the long-term information coming from preceding layers is easy to be weaken or lost in late layers, which is adverse to super-resolving image. To capture more informative features and maintain long-term information for image super-resolution, we propose a channel-wise and spatial feature modulation (CSFM) network in which a sequence of feature-modulation memory (FMM) modules is cascaded with a densely connected structure to transform low-resolution features to high informative features. In each FMM module, we construct a set of channel-wise and spatial attention residual (CSAR) blocks and stack them in a chain structure to dynamically modulate multi-level features in a global-and-local manner. This feature modulation strategy enables the high contribution information to be enhanced and the redundant information to be suppressed. Meanwhile, for long-term information persistence, a gated fusion (GF) node is attached at the end of the FMM module to adaptively fuse hierarchical features and distill more effective information via the dense skip connections and the gating mechanism. Extensive quantitative and qualitative evaluations on benchmark datasets illustrate the superiority of our proposed method over the state-of-the-art methods.