# A cross Transformer for image denoising

arXiv: https://arxiv.org/abs/2310.10408 · HTML: https://mlsanity.com/paper/2310.10408/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 76 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Learning Deep CNN Denoiser Prior for Image Restoration](https://mlsanity.com/paper/1704.03264/) (2017)
- [MemNet: A Persistent Memory Network for Image Restoration](https://mlsanity.com/paper/1708.02209/) (2017)
- [Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration](https://mlsanity.com/paper/1508.02848/) (2015)
- [Multi-level Wavelet-CNN for Image Restoration](https://mlsanity.com/paper/1805.07071/) (2018)
- [Real Image Denoising with Feature Attention](https://mlsanity.com/paper/1904.07396/) (2019)
- [Non-Local Recurrent Network for Image Restoration](https://mlsanity.com/paper/1806.02919/) (2018)
- [Deep Learning on Image Denoising: An overview](https://mlsanity.com/paper/1912.13171/) (2019)
- [Neural Nearest Neighbors Networks](https://mlsanity.com/paper/1810.12575/) (2018)
- [NBNet: Noise Basis Learning for Image Denoising with Subspace Projection](https://mlsanity.com/paper/2012.15028/) (2020)
- [Real-world Noisy Image Denoising: A New Benchmark](https://mlsanity.com/paper/1804.02603/) (2018)
- [Dynamic Attentive Graph Learning for Image Restoration](https://mlsanity.com/paper/2109.06620/) (2021)
- [Deep Graph-Convolutional Image Denoising](https://mlsanity.com/paper/1907.08448/) (2019)
- [Designing and Training of A Dual CNN for Image Denoising](https://mlsanity.com/paper/2007.03951/) (2020)
- [Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising](https://mlsanity.com/paper/1806.05229/) (2018)
- [COLA-Net: Collaborative Attention Network for Image Restoration](https://mlsanity.com/paper/2103.05961/) (2021)
- [Adaptive Image Denoising by Targeted Databases](https://mlsanity.com/paper/1407.5055/) (2014)
- [Enhanced CNN for image denoising](https://mlsanity.com/paper/1810.11834/) (2018)
- [Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation](https://mlsanity.com/paper/2204.13873/) (2022)
- [ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising](https://mlsanity.com/paper/2003.01947/) (2020)
- [A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image Denoising](https://mlsanity.com/paper/2204.12736/) (2022)

## Built on by (most cited first, 6 of 6)

- [Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution](https://mlsanity.com/paper/2404.11273/) (2024)
- [SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection](https://mlsanity.com/paper/2503.24389/) (2025)
- [Heterogeneous window transformer for image denoising](https://mlsanity.com/paper/2407.05709/) (2024)
- [SGDFormer: One-stage Transformer-based Architecture for Cross-Spectral Stereo Image Guided Denoising](https://mlsanity.com/paper/2404.00349/) (2024)
- [Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling](https://mlsanity.com/paper/2412.05943/) (2024)
- [Learned Nonlocal Feature Matching and Filtering for RAW Image Denoising](https://mlsanity.com/paper/2604.17453/) (2026)

## Abstract (arXiv)

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://github.com/hellloxiaotian/CTNet.