PanoPlane achieves up to 17.8% PSNR gains in sparse-view indoor novel view synthesis by using training-free plane-aware panoramic completion to supervise 3D Gaussian Splatting.
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Bovik, Hamid R
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representative citing papers
GuardMarkGS unifies watermarking and adversarial edit deterrence into a single optimization framework for protecting 3D Gaussian Splatting assets.
A new large-scale synthetic multi-task benchmark dataset supplying pixel-perfect depth, domain-shifted night imagery, and multi-scale low-resolution pairs for aerial remote sensing.
MESA restores ancient inscription textures via multi-exemplar style transfer from VGG19 features with per-layer exemplar selection and OCR-derived weights, without any model training.
GeRM learns a distribution transfer vector field via a multi-condition ControlNet to convert physically-based renders into photorealistic images using text prompts and a 50K expert-curated dataset.
LumaFlux is a physically and perceptually guided diffusion transformer for SDR-to-HDR conversion that introduces PGA, PCM, and HDR Residual Coupler modules plus a new training corpus and benchmark, outperforming prior ITM methods.
A sensor-specific calibration pipeline using dark frames produces synthesized noisy RAW images that close 54-64% of the PSNR gap to real noise versus manufacturer profiles, accompanied by the open SNIC dataset of over 6600 paired images.
DRFS is a new inversion-free editing technique for rectified flow models that models source-target velocity discrepancies and applies a time-dependent shift to improve fidelity and unify prior methods like DDS and FlowEdit.
Harder classification tasks produce neural representations whose accuracy collapses under binarization and shuffling while easier tasks remain robust, defining task complexity via the performance gap between full-precision and perturbed networks.
PhotIQA is a new public dataset of 1134 expert-rated photoacoustic images for benchmarking image quality assessment in medical imaging.
Presents SLAM&Render, a robot-recorded benchmark dataset with 40 multi-modal sequences for testing SLAM, novel view synthesis, and Gaussian Splatting under controlled variations in lighting, arrangements, and occlusions.
Proposes a cyclic 2.5D perceptual loss with manufacturer SUVR standardization for T1w MRI to tau PET synthesis, reporting improved regional agreement on ADNI and SCAN cohorts across U-Net, UNETR, SwinUNETR, CycleGAN, and Pix2Pix.
Q-Align trains LMMs on discrete text-defined levels for visual scoring, achieving SOTA on IQA, IAA, and VQA while unifying the tasks in OneAlign.
LiFT factorizes 3D medical volume synthesis into per-slice 2D generation and inter-slice trajectory learning, using a tri-planar drifting loss for unconditional coherence and a z-context mixer for paired translation tasks.
MSIQ is a scale-invariant, model-free quality metric for single image super-resolution using normalized central geometric moments for direct comparison of different-resolution images.
MIRAGE achieves state-of-the-art mental image reconstruction from fMRI on the NSD-Imagery benchmark by using a linear backbone with multi-modal text and image features fed to a diffusion model.
Joint training of NCA rules and SIREN pre-patterns improves robustness, encoding capacity, and symmetry breaking compared to purely self-organizing models by offloading information to initial conditions.
LiBrA-Net achieves real-time native 4K video dehazing via Lie-algebraic bilateral affine fields and releases the first 4K paired dehazing video benchmark with per-frame annotations.
Linear mappings in feature space can reconstruct a wide range of image manipulations including semantic edits, suggesting that feature representations are approximately linearly organized.
AsyncEvGS reconstructs high-fidelity 3D scenes from motion-blurred images by first deblurring via event data then using VGGT-based pose estimation and structure-driven losses inside Gaussian Splatting.
SIFT-VTON adds explicit geometric supervision from SIFT keypoints to diffusion-based virtual try-on to improve spatial alignment and detail preservation.
A new scale-aware diagnostic framework shows that unconstrained diffusion generative models exhibit structural freezing and instability instead of smooth physical responses under multiscale perturbations.
Proposes TinyUSFM-uLPIPS and TinyUSFM-NRQ metrics that show better alignment with segmentation task performance and expert preference than PSNR or VGG-LPIPS in ultrasound imaging.
CAHAL introduces a physics-informed mixture-of-experts super-resolution network for clinical MRI that conditions on resolution and anisotropy and uses edge-penalised, Fourier, and segmentation-guided losses to reduce hallucinations compared with prior generative methods.
citing papers explorer
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AsyncEvGS: Asynchronous Event-Assisted Gaussian Splatting for Handheld Motion-Blurred Scenes
AsyncEvGS reconstructs high-fidelity 3D scenes from motion-blurred images by first deblurring via event data then using VGGT-based pose estimation and structure-driven losses inside Gaussian Splatting.
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Training-inference input alignment outweighs framework choice in longitudinal retinal image prediction
Training-inference input alignment outweighs framework choice for longitudinal retinal image prediction, with deterministic regression matching complex models when acquisition variability dominates disease progression.