  While machine learning approaches have shown remarkable performance in
biomedical image analysis, most of these methods rely on high-quality and
accurate imaging data. However, collecting such data requires intensive and
careful manual effort. One of the major challenges in imaging the Shoot Apical
Meristem (SAM) of Arabidopsis thaliana, is that the deeper slices in the
z-stack suffer from different perpetual quality-related problems like poor
contrast and blurring. These quality-related issues often lead to the disposal
of the painstakingly collected data with little to no control on quality while
collecting the data. Therefore, it becomes necessary to employ and design
techniques that can enhance the images to make them more suitable for further
analysis. In this paper, we propose a data-driven Deep Quantized Latent
Representation (DQLR) methodology for high-quality image reconstruction in the
Shoot Apical Meristem (SAM) of Arabidopsis thaliana. Our proposed framework
utilizes multiple consecutive slices in the z-stack to learn a low dimensional
latent space, quantize it and subsequently perform reconstruction using the
quantized representation to obtain sharper images. Experiments on a publicly
available dataset validate our methodology showing promising results.
