CLUE: Controllable Latent space of Unprompted Embeddings for Diversity Management in Text-to-Image Synthesis

The paper presents the Selectively Suppressed Perfect Reconstruction (SUPER) Decoder Block, designed to enhance U-Net variants used in inverse problems. This method addresses the issue of information loss that limits the recovery of fine details in images. By leveraging the perfect reconstruction property of wavelets, SUPER selectively suppresses redundant features, thus improving representational richness and maintaining computational efficiency. Experiments demonstrate its effectiveness across various benchmarks, particularly in thin-crack segmentation and image denoising tasks.

A new segmentation pipeline utilizing a lightweight U-Net model has been developed for analyzing carbide microstructures in scanning electron microscopy (SEM) images of reactor-pressure-vessel steel. This model, trained on only 10 annotated SEM images, achieves a Dice-Sørensen coefficient of 0.98, significantly surpassing traditional methods that yield a coefficient of 0.85. The approach not only reduces annotation efforts by an order of magnitude but also generalizes to other steel types, enhancing automated carbide quantification for alloy design.

The article discusses a novel method for unsupervised segmentation of Micro-CT scans of polyurethane structures by integrating Hidden Markov Random Fields (HMRF) with a U-Net architecture. This approach aims to enhance segmentation accuracy and speed without the need for large labeled datasets, which are typically required for supervised learning. The integration of HMRF theory with convolutional neural networks (CNNs) represents a significant advancement in the field of material property analysis.