Image calibration using ensemble of transformer and CNN-based frameworks

Image distortion is a problem due to wide field-of-view cameras, and camera calibration is a fundamental step in various applications such as image undistortion, 3D reconstruction, and camera motion estimation to overcome this problem. In image calibration, intrinsic camera parameters such as focal length and distortion are estimated. The quality of the undistorted/enhanced image depends on the correctness of focal length and distortion. However, existing methods consist of two approaches: checkerboard, which requires manual interaction, and others are deep learning approaches. Most Deep Learning approaches are based on the Convolution Neural Network (CNN) framework, and it fails to capture the long-term dependency in a distorted image. This paper proposes a fully automated EnsembleNet method to infer the focal length and distortion parameters to overcome this problem. The proposed model extracts various contexts (local patches) by exploiting ViT(Vision Transformer) and spatial features from various CNN-based models using a single input image. The proposed model uses the differential evolution (DE) approach to learn the ensemble weights. The experiments show that the proposed EnsembleNet outperforms the state-of-the-art deep learning-based models in terms of mean squared error.