Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space
This addresses image quality issues in medical diagnosis and disease analysis, but it is incremental as it builds on existing autoencoder techniques with regularization.
The paper tackles the problem of defocus blur in microscopic images by proposing a method that both synthesizes blur and deblurs images using autoencoders with linear latent spaces, enabling flexible blur level manipulation to enhance image quality and data variety.
Though modern microscopes have an autofocusing system to ensure optimal focus, out-of-focus images can still occur when cells within the medium are not all in the same focal plane, affecting the image quality for medical diagnosis and analysis of diseases. We propose a method that can deblur images as well as synthesize defocus blur. We train autoencoders with implicit and explicit regularization techniques to enforce linearity relations among the representations of different blur levels in the latent space. This allows for the exploration of different blur levels of an object by linearly interpolating/extrapolating the latent representations of images taken at different focal planes. Compared to existing works, we use a simple architecture to synthesize images with flexible blur levels, leveraging the linear latent space. Our regularized autoencoders can effectively mimic blur and deblur, increasing data variety as a data augmentation technique and improving the quality of microscopic images, which would be beneficial for further processing and analysis.