H-2DGS addresses the need for sharp, photorealistic neurosurgical surface reconstruction that can fuse with MRI and tractography without obscuring internal brain structures. The authors propose a three-stage pipeline that bootstraps appearance with conventional 3D Gaussian Splatting, contracts volumetric density along camera rays using differentiable quantiles, and converts the fused TSDF surface into curved height-field Gaussian patches. By explicitly representing a thin, deformable cortical sheet rather than a diffuse volume, the method targets geometric precision and compactness at the cost of some rendering fidelity.

Multibeam imaging sonar is widely used for underwater perception, but its intensity images are degraded by coupled coherent speckle and system-induced blur, and clean reflectivity ground truth is almost never available. This paper derives the conditional-mean forward operator $A x = x * |h|^2$ from a stochastic coherent image-formation model and uses it to train a self-supervised reconstruction network from registered multi-frame observations. By projecting one frame's latent reconstruction through $A$ and matching it to a second registered frame, then regularizing with an adapted robust equivariant imaging loss, the framework targets joint despeckling and deblurring without any clean targets. The work matters because it replaces ad hoc intensity-domain speckle models with a physics-consistent inverse problem for real sonar platforms.

QICOM is an open-source desktop application that unites quantitative medical image comparison, paired radiomics, and blinded observer assessment into a single reproducible workflow. It tackles the fragmentation problem that forces researchers to juggle separate tools and scripts, losing the link between images, settings, and results. By saving native projects, configurations, and blinded response mappings, it enables any comparison to be reopened and recalculated, which is essential for trustworthy evaluation of image-processing methods and acquisition protocols.

Accurate intra-hour solar irradiance forecasting from ground-based sky images is essential for grid balancing and storage scheduling as PV penetration rises. GateFormer tackles this by embedding observed global horizontal irradiance directly into a Vision Transformer token stream, then producing two complementary forecasts—a multi-step trajectory and a direct final-horizon displacement—that are adaptively blended and residual-corrected in prediction space. The core claim is that this forecast-space decomposition, rather than conventional latent fusion or ensemble combination, yields consistent gains across three heterogeneous sky-imaging datasets.

Iterative tractography must reconcile noisy local diffusion signals with globally plausible fiber pathways, yet standard deep learning models optimize only a local next-step loss. This paper tackles that gap by systematically evaluating RNN and Transformer architectures and introducing a generation-validation (GV) phase that supervises training at the streamline level via connectivity scores. It reports top scores on the ISMRM 2015 phantom and provides open-source models, while also arguing that current phantoms and in vivo evaluation tools are too limited for definitive benchmarking.

Sparse-view 3D reconstruction from only a handful of images is fundamentally ill-posed: standard methods overfit to individual views and leave unseen regions as holes or floaters. HierGF tackles this with a hierarchical 3D Gaussian Splatting pipeline that separates a coarse geometric backbone from a fine-scale field which inherits only spatial means and colors while re-estimating shape and appearance under enhanced pseudo-supervision. Geometry-consistent densification and confidence-weighted diffusion priors are then used to fill gaps and suppress artifacts in under-sampled regions. The authors report strong gains on MipNeRF360, OmniObject3D, LLFF and DTU, alongside a sub-11-minute per-scene reconstruction time on an A100 GPU.

UniTrackPLA tackles the problem of unifying instruction-guided navigation and dynamic person tracking—two embodied tasks traditionally handled by separate policies—within a single panoramic vision-language-action framework. Its core idea is to fuse omnidirectional perception with a shared continuous waypoint prediction space, using Panoramic-Aware Encoding to adapt perspective pretrained encoders to 360° observations and World-Action Consistency to adaptively decide when to reuse waypoint chunks or replan. This matters because it removes heading-dependent initialization constraints and offers a step toward general-purpose robots that can follow both linguistic routes and moving humans with one model.

MorphoBranch tackles the problem of accurately quantifying fine, weakly labeled, and densely branched cellular structures—such as neuronal axons and microglial processes—from fluorescence microscopy images. The core idea is an integrated workbench that pairs a deterministic image-to-graph Morphometry Engine (multiscale ridge evidence, hysteresis segmentation, and evidence-constrained skeleton refinement) with an optional LLM-assisted Refinement Engine that translates natural-language instructions into editable, human-reviewable analysis plans. This separation of flexible interaction from deterministic computation aims to reduce operator-dependent bias while keeping morphometric workflows inspectable and reproducible.

This paper proposes the Universal Normal Embedding (UNE) hypothesis: that generative models and vision encoders, despite different objectives, both approximate noisy linear projections of a shared Gaussian latent space. The authors argue that DDIM-inverted diffusion noise and encoder embeddings (CLIP, DINO) share this approximately Gaussian geometry, enabling linear semantic editing without architectural changes. They introduce NoiseZoo, a dataset of paired latents, to empirically test whether generative noise encodes semantic structure comparable to foundation encoders.

The paper tackles the computational bottleneck of radiative transfer models (RTMs) for hyperspectral image (HSI) generation by proposing a VAE-based emulation framework that learns latent representations conditioned on biophysical parameters. It introduces both pixel-to-pixel (P2P) and fully convolutional (FC-VAE) variants, trained via either direct one-step mapping or a two-step pretraining strategy that decouples representation learning from parameter-to-latent interpolation. The work is significant for remote sensing applications as it provides empirical evidence that optimal emulator architecture depends critically on whether the target data is simulated (where P2P excels) or real-world imagery (where FC-VAE-pre dominates), and demonstrates that emulated data preserves downstream utility for parameter retrieval tasks.

This paper tackles unregistered hyperspectral-multispectral image fusion (HMF), where spatially misaligned images with partial overlap must be mutually super-resolved without training data or co-registration. The authors propose FRESCO, a two-stage unsupervised framework that uses coupled block-term tensor decomposition (BTD) for MSI spectral super-resolution and latent-space adversarial learning for HSI spatial super-resolution. The work is notable for offering the first theoretical recoverability guarantees in the unregistered setting, addressing a practically important gap in remote sensing.

HMS-VesselNet addresses the challenge of segmenting thin peripheral retinal vessels in fundus images—a critical task for early diabetic retinopathy detection where standard overlap losses fail due to class imbalance and topological fragmentation. The paper proposes a four-scale hierarchical Attention U-Net architecture with learned fusion weights, combining Dice, binary cross-entropy, and centerline Dice ($\text{clDice}$) losses alongside hard example mining to boost sensitivity on sub-2-pixel vessels. Evaluated on 68 images from DRIVE, STARE, and CHASE_DB1 via 5-fold cross-validation and leave-one-dataset-out protocols, the model achieves $90.78\pm1.42\%$ Sensitivity, demonstrating that explicit topology preservation and targeted hard example oversampling can recover fine vascular structures missed by standard area-based losses.

OrbitStream addresses adaptive 360° video streaming for teleoperation by proposing a training-free framework that combines semantic scene understanding with robust control theory. It formulates viewport prediction as a Gravitational Viewport Prediction (GVP) problem where semantic objects (pedestrians, vehicles) generate potential fields that "attract" user gaze with task-relevant mass, while a Saturation-Based Proportional-Derivative (PD) Controller handles bitrate adaptation. This offers an interpretable, zero-shot alternative to black-box Deep Reinforcement Learning methods for safety-critical systems where deployment constraints prohibit lengthy training.

CICTM addresses deformable brain MRI registration by combining transformer-based global context modeling with cycle inverse-consistency constraints. The core idea uses a Swin-UNet to jointly estimate forward and backward deformation fields, penalizing inconsistencies at both image and flow levels while enforcing topology preservation via Jacobian regularization. The work matters for large-scale neuroimaging studies where deformation stability and physical plausibility are as important as alignment accuracy.