IEEE SPL Article

We introduce ICE-TIDE, a method for cryogenic electron tomography (cryo-ET) that simultaneously aligns observations and reconstructs a high-resolution volume. The alignment of tilt series in cryo-ET is a major problem limiting the resolution of reconstructions. ICE-TIDE relies on an efficient coordinate-based implicit neural representation of the volume which enables it to directly parameterize deformations and align the projections.

Magnetic Resonance Imaging (MRI) is a widely used imaging technique, however it has the limitation of long scanning time. Though previous model-based and learning-based MRI reconstruction methods have shown promising performance, most of them have not fully utilized the edge prior of MR images, and there is still much room for improvement.

While snapshot hyperspectral cameras are cheaper and faster than imagers based on pushbroom or whiskbroom spatial scanning, the output imagery from a snapshot camera typically has different spectral bands mapped to different spatial locations in a mosaic pattern, requiring a demosaicing process to be applied to generate the desired hyperspectral image with full spatial and spectral resolution.

We consider the problem of recovering off-the-grid spikes from linear measurements in the context of Single Molecule Localization Microscopy (SMLM). State of the art model-based methods such as Over-Parametrized Continuous Orthogonal Matching Pursuit (OP-COMP) with Projected Gradient Descent (PGD) have been shown to successfully recover those signals. 

Single-satellite geolocation achieves effective localization of ground electromagnetic interference (EMI) signals with a low cost compared to the multi-satellite counterparts. In such systems, the Doppler and Doppler rate are commonly exploited to extract the information of the ground EMI sources and the constrained Unscented Kalman filter (cUKF) is found effective to provide instantaneous EMI locations over time. 

Scene-Text Visual Question Answering (STVQA) is a comprehensive task that requires reading and understanding the text in images to answer the question. Existing methods of exploring the vision-language relationships between questions, images, and scene text have achieved impressive results. However, these studies heavily rely on auxiliary modules, such as external OCR systems and object detection networks, making the question-answering process cumbersome and highly dependent.

Blind image quality assessment (BIQA) is crucial for user satisfaction and the performance of various image processing applications. Most BIQA methods directly use the pre-trained model to extract features and then perform feature fusion. However, the features extracted by pre-trained models may contain irrelevant information to BIQA. Although some methodspre-train the feature extraction network from scratch, these approaches raise computational costs and resource demands.

Adaptive importance sampling (AIS) methods provide a useful alternative to Markov Chain Monte Carlo (MCMC) algorithms for performing inference of intractable distributions. Population Monte Carlo (PMC) algorithms constitute a family of AIS approaches which adapt the proposal distributions iteratively to improve the approximation of the target distribution. 

Vision Transformer (ViT)-based image super-resolution (SR) methods have achieved impressive performance and surpassed CNN-based SR methods by utilizing Multi-Head Self-Attention (MHSA) to model long-range dependencies. However, the quadratic complexity of MHSA and the inefficiency of non-parallelized window partition seriously affect the inference speed, hindering these SR methods from being applied to application scenarios requiring speed and quality.

Learning-based approaches inspired by the scattering model for enhancing underwater imagery have gained prominence. Nevertheless, these methods often suffer from time-consuming attributable to their sizable model dimensions. Moreover, they face challenges in adapting unknown scenes, primarily because the scattering model's original design was intended for atmospheric rather than marine condition.