The flip-chip (FC) technology is widely used in microelectronic packaging. As the dimensions and pitch of the chip solder bumps are getting smaller, defect inspection becomes increasingly challenging. In this paper, scanning acoustic microscopy (SAM) was used for the non-destructive inspection of FC packages. However, the detection accuracy may be limited by a low resolution of the SAM image. Therefore, the image super-resolution (SR) method was employed to enhance the SAM image quality. The modified very-deep SR (VDSR) algorithm, based on a convolutional neural network (CNN), was investigated to reconstruct high resolution acoustic micrographs. In addition, a CNN-based classification model was designed for the classification of the solder joints. The results obtained in the present study demonstrate that the improved VDSR combined with the CNN classification model provides excellent performance for improving the SAM detection accuracy of the inspection of flip chip packages.