Deep Learning Based Light-Field Refocusing of Burner Flames

A light-field (LF) camera with refocusing algorithms enables the reconstruction of images at different depths. Using images of various depths, the intensity distribution of each image section can then be calculated, which is particularly useful for analyzing complex scenes such as flames. However, LF images cannot be refocused in real time using conventional algorithms due to high computational demands. In this paper, we present a deep learning (DL)-based method for real-time refocusing of LF images of a burner flame, leveraging depth information obtained from the captured LF images. A Convolutional Neural Network (CNN) model is developed using a transfer learning approach, where a pretrained ResNet-50 model is extended with custom convolutional layers to perform the refocusing task. Synthetic datasets are generated using a ray tracing simulation based on the ray transfer matrix (RTM) method to train the model. The trained model produces four refocused outputs corresponding to distinct depth planes. The proposed method eliminates the need for the computationally intensive shift-and-sum algorithm traditionally applied to LF images. Simulation results show that the method accurately refocuses both geometric and flame structures, preserving depth-aware detail across planes. This approach has strong potential for enabling real-time, nonintrusive diagnostics in combustion systems and other dynamic, depth-varying environments.