A Noise-Aware Convolutional Neural Network for Noise Reduction and Resolution Enhancement in Chest X-Ray Images
Received: 23 September 2025 | Revised: 17 December 2025 | Accepted: 24 December 2025 | Online: 4 April 2026
Corresponding author: Laxmibai
Abstract
Low-resolution medical images, especially Chest X-Rays (CXRs), often suffer from noise and blurriness, hindering accurate diagnosis. This study introduces the Noise-Aware Convolutional Neural Network (NA-CNN) architecture to address this issue. The objective is to enhance image quality by eliminating noise and converting low-resolution images into high-resolution images. The methodology involves a Convolutional Neural Network (CNN)-based model integrated with sparse coding reconstruction, adaptive downsampling, and nonlinear mapping. Evaluations were conducted on a high-performance system using the COVID-19 Radiography dataset. The results demonstrated that NA-CNN consistently outperformed the existing CNN model, achieving higher Peak Signal-to-Noise-Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values across various noise levels, indicating superior image quality and structural fidelity. The novelty of this work lies in its innovative architecture that combines a CNN with adaptive techniques, resulting in efficient and high-quality image enhancement. NA-CNN's robustness and efficiency make it a valuable tool for medical image processing, providing significant advancements over existing methods.
Keywords:
low-resolution images, Noise-Aware Convolutional Neural Network (NA-CNN), sparse coding reconstruction, Chest X-Rays (CXRs), PSNR, SSIMDownloads
References
G. S. Gould et al., "Recognising the importance of chronic lung disease: a consensus statement from the Global Alliance for Chronic Diseases (Lung Diseases group)," Respiratory Research, vol. 24, no. 1, Jan. 2023, Art. no. 15. DOI: https://doi.org/10.1186/s12931-022-02297-y
G. Rasool et al., "COVID-19: A threat to the respiratory system," International Journal of Immunopathology and Pharmacology, vol. 38, Aug. 2024, Art. no. 03946320241310307. DOI: https://doi.org/10.1177/03946320241310307
N. C. Kundur, B. C. Anil, P. M. Dhulavvagol, R. Ganiger, and B. Ramadoss, "Pneumonia Detection in Chest X-Rays using Transfer Learning and TPUs," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11878–11883, Oct. 2023. DOI: https://doi.org/10.48084/etasr.6335
X. Jiang, Y. Zhu, B. Zheng, and D. Yang, "Images denoising for COVID-19 chest X-ray based on multi-resolution parallel residual CNN," Machine Vision and Applications, vol. 32, no. 4, June 2021, Art. no. 100. DOI: https://doi.org/10.1007/s00138-021-01224-3
A. Giełczyk, A. Marciniak, M. Tarczewska, and Z. Lutowski, "Pre-processing methods in chest X-ray image classification," Plos One, vol. 17, no. 4, Apr. 2022, Art. no. e0265949. DOI: https://doi.org/10.1371/journal.pone.0265949
H. N. Monday et al., "COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network," Diagnostics, vol. 12, no. 3, Mar. 2022, Art. no. 741. DOI: https://doi.org/10.3390/diagnostics12030741
I. U. Khan et al., "An effective approach to address processing time and computational complexity employing modified CCT for lung disease classification," Intelligent Systems with Applications, vol. 16, Nov. 2022, Art. no. 200147. DOI: https://doi.org/10.1016/j.iswa.2022.200147
H. Sun et al., "Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification," Quantitative Imaging in Medicine and Surgery, vol. 13, no. 1, pp. 39416–39416, Jan. 2023. DOI: https://doi.org/10.21037/qims-22-610
J. S. Jennifer and T. S. Sharmila, "A Neutrosophic Set Approach on Chest X-rays for Automatic Lung Infection Detection," Information Technology and Control, vol. 52, no. 1, pp. 37–52, Mar. 2023. DOI: https://doi.org/10.5755/j01.itc.52.1.31520
A. Khishigdelger, A. Salem, and H.-S. Kang, "Elevating Chest X-ray Image Super-Resolution with Residual Network Enhancement," Journal of Imaging, vol. 10, no. 3, Mar. 2024, Art. no. 64. DOI: https://doi.org/10.3390/jimaging10030064
Md. Z. Hasan et al., "Fast and Efficient Lung Abnormality Identification With Explainable AI: A Comprehensive Framework for Chest CT Scan and X-Ray Images," IEEE Access, vol. 12, pp. 31117–31135, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3369900
S. Rani and N. Kaur, "Medical X-ray images enhancement based on super resolution convolution neural network," International Journal of Informatics and Communication Technology, vol. 13, no. 2, pp. 257–263, Aug. 2024. DOI: https://doi.org/10.11591/ijict.v13i2.pp257-263
J. C. Hauffen, L. Kästner, S. Ahmadi, P. Jung, G. Caire, and M. Ziegler, "Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging," Sensors, vol. 22, no. 15, July 2022, Art. no. 5533. DOI: https://doi.org/10.3390/s22155533
"COVID-19 Radiography Database." Kaggle. [Online]. Available: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database.
M. E. H. Chowdhury et al., "Can AI Help in Screening Viral and COVID-19 Pneumonia?," IEEE Access, vol. 8, pp. 132665–132676, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3010287
T. Rahman et al., "Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images," Computers in Biology and Medicine, vol. 132, May 2021, Art. no. 104319. DOI: https://doi.org/10.1016/j.compbiomed.2021.104319
Downloads
How to Cite
License
Copyright (c) 2026 Laxmibai, Vinita Patil
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
