Camera calibration method based on fast defocus estimation and combined small targets

Abstract Accurate visual measurement depends on precise camera calibration. For cameras with a large field of view (FOV), combined small targets (CST) are commonly used to construct a large calibration object, balancing accuracy and flexibility. However, calibration accuracy is significantly affected when the calibration object is defocused. To overcome this challenge, this paper proposes a CST-based calibration method incorporating defocus deblurring. An image restoration method based on fast defocus estimation is introduced to efficiently restore defocus blur. The method estimates defocus blur through dual-scale re-blurring and region-level transductive inference, and then performs deconvolution accordingly. Building upon this, a novel calibration strategy based on defocus estimation and CST is developed. Multiple small targets (STs) are placed within the camera FOV, and images are captured by adjusting the relative pose between the camera and CST. To enhance feature extraction accuracy, deblurring is applied to defocused ST regions. Extracted features from each ST are then integrated using a global nonlinear optimization algorithm, achieving high-precision calibration. Experimental results demonstrate that the proposed method effectively mitigates the impact of CST defocus on calibration precision, with good stability and computational efficiency. This study provides reliable technical support for calibrating cameras with a large FOV in non-ideal imaging environments and holds significant application potential.