Stripe Noise

Stripe noise, a prevalent artifact in various imaging modalities (e.g., infrared, light-sheet microscopy), degrades image quality by introducing unwanted vertical lines. Current research focuses on developing sophisticated deep learning models, such as CycleGAN variations and U-Net architectures, often incorporating wavelet transforms and graph neural networks, to effectively remove these stripes while preserving image details. These advancements aim to improve image analysis and interpretation across diverse scientific fields and applications, ranging from infrared object detection to biomedical imaging. The ultimate goal is to create robust and efficient destriping algorithms that accurately restore images without introducing new artifacts or losing crucial information.