Publication

Abstract : This study introduces FringeNet, an innovative deep learning-based cyclic model to enhance the fringe order demodulation from single isochromatic images. A Continuity-Imposed Hybrid Cyclic Loss (CHCL) function, which combines Mean Squared Error (MSE), Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and a continuity loss is proposed for optimizing multiple objectives, while enforcing the fringe order continuity in the isochromatic data. The proposed FringeNet is trained using available open-source ischromatic image dataset and the effectiveness of the model is validated using realistic experimental isochromatics. The FringeNet demonstrates a 92.4% improvement in MSE over existing methods, indicating substantial gains in predictive accuracy and model robustness. Additionally, a 16.19% enhancement in PSNR is observed, highlighting superior fidelity compared to existing approaches. The cyclic model employed in FringeNet represents a significant advancement in enhancing the deep learning-based predictive modeling used in the field of digital photoelasticity.