Super-Resolution Reconstruction of Part Images Using Adaptive Multi-Scale Object Tracking

Computer vision-based part surface inspection is widely used for quality evaluation. However, challenges such as low image quality, caused by factors like inadequate acquisition equipment, camera vibrations, and environmental conditions, often lead to reduced detection accuracy. Although super-resolution reconstruction can enhance image quality, existing methods face issues such as limited accuracy, information distortion, and high computational cost. To overcome these challenges, we propose a novel super-resolution reconstruction method for part images that incorporates adaptive multi-scale object tracking. Our approach first adaptively segments the input sequence of part images into blocks of varying scales, improving both reconstruction accuracy and computational efficiency. Optical flow is then applied to estimate the motion parameters between sequence images, followed by the construction of a feature tracking and sampling model to extract detailed features from all images, addressing information distortion caused by pixel misalignment. Finally, a non-linear reconstruction algorithm is employed to generate the high-resolution target image. Experimental results demonstrate that our method achieves superior performance in terms of both quantitative metrics and visual quality, outperforming existing methods. This contributes to a significant improvement in subsequent part detection accuracy and production efficiency.