mlsanityData to 7 Oct 2026

Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs

arXiv 2310.07245 ↗First posted 11 Oct 2023cs.CV cs.LG eess.IVcited by 4

Not on the map. Why?

Abstract (arXiv)

Visual crowd counting estimates the density of the crowd using deep learning models such as convolution neural networks (CNNs). The performance of the model heavily relies on the quality of the training data that constitutes crowd images. In harsh weather such as fog, dust, and low light conditions, the inference performance may severely degrade on the noisy and blur images. In this paper, we propose the use of Pix2Pix generative adversarial network (GAN) to first denoise the crowd images prior to passing them to the counting model. A Pix2Pix network is trained using synthetic noisy images generated from original crowd images and then the pretrained generator is then used in the inference engine to estimate the crowd density in unseen, noisy crowd images. The performance is tested on JHU-Crowd dataset to validate the significance of the proposed method particularly when high reliability and accuracy are required.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 17 of its 41 references that are in the map. First posted on arXiv 11 Oct 2023.

Builds on

17 of 41 references.

References · 17

  1. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  3. 2014Conditional Generative Adversarial Netscited by 2,152
  4. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  5. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  6. 2018CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenescited by 267
  7. 2018Context-Aware Crowd Countingcited by 150
  8. 2020JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Methodcited by 109
  9. 2019Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networkcited by 56
  10. 2016CrowdNet: A Deep Convolutional Network for Dense Crowd Countingcited by 52
  11. 2017CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Countingcited by 41
  12. 2017Multi-scale Convolutional Neural Networks for Crowd Countingcited by 26
5 more
  1. 2022Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectivescited by 16
  2. 2019Unpaired image denoising using a generative adversarial network in X-ray CTcited by 10
  3. 2019Deep Density-aware Count Regressorcited by 6
  4. 2023LCDnet: A Lightweight Crowd Density Estimation Model for Real-time Video Surveillancecited by 6
  5. 2022DroneNet: Crowd Density Estimation using Self-ONNs for Dronescited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2025Bayesian Optimization for Controlled Image Editing via LLMscited by 11
  2. 2024Multimodal Crowd Counting with Pix2Pix GANscited by 2
  3. 2024Denoising diffusion models for high-resolution microscopy image restorationcited by 0
  4. 2026Pix2Pix-Hybrid: Structure-Guided Conditional Synthesis of Hajj Crowd Images with Multi-Channel Conditioning and Weak Attribute Supervisioncited by 0