Noise Suppressed Image Reconstruction for Quanta Image Sensors Based on Transformer Neural Networks
Year: 2025
Type: article
Abstract: The photon detection capability of quanta image sensors make them an optimal choice for low-light imaging. To address Possion noise in QIS reconstruction caused by spatio-temporal oversampling characteristic, a deep learning-based noise suppression reconstruction method is proposed in this paper. The proposed neural network integrates convolutional neural networks and Transformers. Its architecture combines the Anscombe transformation with serial and parallel modules to enhance denoising performance and adaptability across various scenarios. Experimental results demonstrate that the proposed method effectively suppresses noise in QIS image reconstruction. Compared with representative methods such as TD-BM3D, QIS-Net and DPIR, our approach achieves up to 1.2 dB improvement in PSNR, demonstrating superior reconstruction quality.
Source: Journal of Imaging
Authors: Guanjie Wang, Zhiyuan Gao
Institution: Tianjin University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W4410479477