A novel resolution-improvement method for integral imaging microscopy that combines an intermediate-view image generation and the deep learning-based super-resolution methods. After the IVEIs are generated once, the orthographic-view image is reconstructed, and the pre-trained deep learning-based SRMD model improves the resolution of each sub-image by two or four times according to the scale factor, in each axis.

A novel resolution-enhancement method for an integral imaging microscopy that applies interpolation and deep learning is proposed, and the complete system with both hardware and software components is implemented. The resolution of the captured elemental image array is increased by generating intermediate-view elemental images between each neighboring elemental image, and an orthographic-view visualization of the specimen is reconstructed. Then, a deep learning algorithm is used to generate maximum possible resolution for each reconstructed directional-view image with improved quality. Since a pre-trained model is applied, the proposed system processes the images directly without data training. The experimental results indicate that the proposed system produces resolution-enhanced directional-view images, and quantitative evaluation methods for reconstructed images such as the peak signal-to-noise ratio and the power spectral density confirm that the proposed system provides improvements in image quality.