Binocular camera calibration is the foundation of stereo vision research, and its accuracy is the key to achieve precision in vision measurement. The basis of camera calibration is image corner extraction, but in real applications, the accuracy of detected corner points is often low due to unclear acquisition of images due to external influences, which affects calibration accuracy. To solve the problem of low-quality corner detection in terms of the feature level, an end-to-end algorithm based on super-resolution subpixel corner detection is proposed. First, the fuzzy kernel of a low-resolution image is estimated using the blind hyperspectral part, and its features are fused to reconstruct its high-resolution version. Subsequently, the subpixel position of the corner points is obtained. Finally, a binocular camera is calibrated with high accuracy and tested via ranging experiments. Experimental results show that the proposed subpixel corner detection method based on super-resolution has advantages in real scenarios.