# Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging

arXiv: https://arxiv.org/abs/2104.02873 · HTML: https://mlsanity.com/paper/2104.02873/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Deep Learning from Noisy Image Labels with Quality Embedding](https://mlsanity.com/paper/1711.00583/) (2017)
- [Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning](https://mlsanity.com/paper/1907.03029/) (2019)
- [Iterative Joint Image Demosaicking and Denoising using a Residual Denoising Network](https://mlsanity.com/paper/1807.06403/) (2018)
- [NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising](https://mlsanity.com/paper/1906.06834/) (2019)
- [External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising](https://mlsanity.com/paper/1705.04505/) (2017)
- [Computational ghost imaging using deep learning](https://mlsanity.com/paper/1710.08343/) (2017)
- [Ultra-high-speed color imaging with single-pixel detectors under low light level](https://mlsanity.com/paper/1907.09517/) (2019)

## Abstract (arXiv)

Ghost imaging (GI) has been paid attention gradually because of its lens-less imaging capability, turbulence-free imaging and high detection sensitivity. However, low image quality and slow imaging speed restrict the application process of GI. In this paper, we propose a improved GI method based on Denoising Convolutional Neural Networks (DnCNN). Inspired by the corresponding between input (noisy image) and output (residual image) in DnCNN, we construct the mapping between speckles sequence and the corresponding noise distribution in GI through training. Then, the same speckles sequence is employed to illuminate unknown targets, and a de-noising target image will be obtained. The proposed method can be regarded as a general method for GI. Under two sampling rates, extensive experiments are carried out to compare with traditional GI method (basic correlation and compressed sensing) and DnCNN method on three data sets. Moreover, we set up a physical GI experiment system to verify the proposed method. The results show that the proposed method achieves promising performance.