Shanmugaraja Thangavel, Karthikeyan Natesapillai, Karthik Subburathinam, Bharathi Balamurugan
Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.
Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, Tamil Nadu, India.
PLoS One. 2025 May 12;20(5):e0317758. doi: 10.1371/journal.pone.0317758. eCollection 2025.
Eliminating flickering from digital images captured by cameras equipped with a rolling shutter is of paramount importance in computer vision applications. The ripple effect observed in an individual image is a consequence of the non-synchronized exposure of rolling shutters utilized in CMOS sensor-based cameras. To date, there have been only a limited number of studies focusing on the mitigation of flickering in single images. Furthermore, it is more feasible to eliminate these flickers with prior knowledge, such as camera specifications or matching images. To solve these problems, we present an unsupervised framework Super-Resolution Generative Adversarial Networks and Partition-Based Adaptive Filtering Technique (SRGAN-PBAFT) trained on unpaired images from end to end Deflickering of a single image. Flicker artifacts, which are commonly caused by dynamic lighting circumstances and sensor noise, can severely reduce an image's visual quality and authenticity. To enhance image resolution SRGAN is used, while Partition based Adaptive Filtering technique detects and mitigates flicker distortions successfully. Combining the strengths of deep learning and adaptive filtering results in a potent approach for restoring image integrity. Experimental results shows that the Proposed SRGAN-PBAFT method is effective, with major improvements in visual quality and flicker aberration reduction compared to existing methods.
在计算机视觉应用中，消除配备卷帘快门的相机所拍摄数字图像中的闪烁至关重要。在单个图像中观察到的波纹效应是基于CMOS传感器的相机中使用的卷帘快门非同步曝光的结果。迄今为止，仅有有限数量的研究专注于减轻单个图像中的闪烁。此外，利用诸如相机规格或匹配图像等先验知识来消除这些闪烁更为可行。为了解决这些问题，我们提出了一种无监督框架——超分辨率生成对抗网络和基于分区的自适应滤波技术（SRGAN-PBAFT），该框架在未配对图像上进行端到端训练，用于单个图像的去闪烁。闪烁伪像通常由动态光照环境和传感器噪声引起，会严重降低图像的视觉质量和真实性。为了提高图像分辨率，使用了SRGAN，而基于分区的自适应滤波技术成功地检测并减轻了闪烁失真。将深度学习和自适应滤波的优势相结合，形成了一种恢复图像完整性的有效方法。实验结果表明，所提出的SRGAN-PBAFT方法是有效的，与现有方法相比，在视觉质量和闪烁像差减少方面有显著改进。
