Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space

TL;DR

This paper introduces a method using regularized autoencoders to synthesize and deblur microscopic images by exploring linear relations in latent space, enhancing image quality and data augmentation.

Contribution

It presents a novel approach that leverages linear latent space interpolation/extrapolation for flexible defocus blur synthesis and deblurring in microscopy images.

Findings

Effective blur synthesis and deblurring demonstrated

Improved image quality for medical diagnosis

Enhanced data augmentation for microscopic image analysis

Abstract

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.