mlsanityData to 14 Oct 2026

A comparison study of CNN denoisers on PRNU extraction

arXiv 2112.02858 ↗First posted 6 Dec 2021eess.IV cs.CV cs.MMcited by 1

Not on the map. Why?

Abstract (arXiv)

Performance of the sensor-based camera identification (SCI) method heavily relies on the denoising filter in estimating Photo-Response Non-Uniformity (PRNU). Given various attempts on enhancing the quality of the extracted PRNU, it still suffers from unsatisfactory performance in low-resolution images and high computational demand. Leveraging the similarity of PRNU estimation and image denoising, we take advantage of the latest achievements of Convolutional Neural Network (CNN)-based denoisers for PRNU extraction. In this paper, a comparative evaluation of such CNN denoisers on SCI performance is carried out on the public "Dresden Image Database". Our findings are two-fold. From one aspect, both the PRNU extraction and image denoising separate noise from the image content. Hence, SCI can benefit from the recent CNN denoisers if carefully trained. From another aspect, the goals and the scenarios of PRNU extraction and image denoising are different since one optimizes the quality of noise and the other optimizes the image quality. A carefully tailored training is needed when CNN denoisers are used for PRNU estimation. Alternative strategies of training data preparation and loss function design are analyzed theoretically and evaluated experimentally. We point out that feeding the CNNs with image-PRNU pairs and training them with correlation-based loss function result in the best PRNU estimation performance. To facilitate further studies of SCI, we also propose a minimum-loss camera fingerprint quantization scheme using which we save the fingerprints as image files in PNG format. Furthermore, we make the quantized fingerprints of the cameras from the "Dresden Image Database" publicly available.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 37 references that are in the map. First posted on arXiv 6 Dec 2021.

Builds on

11 of 37 references.

References · 11

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  5. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
  6. 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 510
  7. 2018Toward Convolutional Blind Denoising of Real Photographscited by 274
  8. 2019Deep Learning on Image Denoising: An overviewcited by 128
  9. 2018Noiseprint: a CNN-based camera model fingerprintcited by 114
  10. 2020Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generationcited by 101
  11. 2020SPN-CNN: Boosting Sensor-Based Source Camera Attribution With Deep Learningcited by 3

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025PRNU-Bench: A Novel Benchmark and Model for PRNU-Based Camera Identificationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --
  2. 2026PocketDVDNet: Realtime Video Denoising for Real Camera Noise
  3. 2026YeTI: You Only Need Two Noisy Images for Real-World sRGB Noise Generation
  4. 2025TinySR: Pruning Diffusion for Real-World Image Super-Resolution
  5. 2024DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization
  6. 2025LinDeps: A Fine-tuning Free Post-Pruning Method to Remove Layer-Wise Linear Dependencies with Guaranteed Performance Preservation
  7. 2026TM-BSN: Triangular-Masked Blind-Spot Network for Real-World Self-Supervised Image Denoising
  8. 2022CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models