The paper introduces RetinaDiff, a physics‑informed conditional diffusion framework for retinal temporal laser speckle contrast imaging (tLSCI). It first stabilizes speckle sequences via phase‑correlation registration, then uses a motion‑corrected contrast prior to guide a diffusion model that reconstructs high‑quality tLSCI maps from only a few frames. Experiments on an in‑house retinal LSCI system show significant improvements in SSIM, PSNR, and FID compared to conventional five‑frame reconstruction, even in challenging cases.
arXiv:2607. 10140v1 Announce Type: cross Abstract: Existing optical flow methods broadly follow two paradigms: iterative optimization and diffusion-based estimation.
arXiv:2512.23709v3 Announce Type: replace Abstract: Diffusion-based video super-resolution (VSR) methods deliver strong perceptual quality but are often unsuitable for latency-sensitive scenarios due...
arXiv:2609.07336v2 Announce Type: replace Abstract: Morphology of the sub-basal nerve plexus (SNP) reflects peripheral nerve health, and corneal confocal microscopy (CCM) provides an important means...
arXiv:2606. 05375v1 Announce Type: cross Abstract: Optical coherence tomographic angiography (OCTA) is a powerful technique for imaging retinal microvasculature.
arXiv:2609.24049v1 Announce Type: new Abstract: Accurate retinal vessel segmentation is important for computer-aided ophthalmic analysis, yet thin vessels, low contrast, and severe foreground-backgro...
arXiv:2608. 19860v1 Announce Type: new Abstract: Single-shot exposure correction aims to map an arbitrarily degraded image---whether under-exposed, over-exposed, or a spatial mixture of both---to a well-exposed output from a single capture.
The paper introduces FastFlowGS, a streaming 4D Gaussian Splatting method that reconstructs fast-moving subjects from a sparse set of external cameras, and Monaco4D, a photorealistic Unreal Engine 5 benchmark featuring Formula 1 sequences with dense ground truth. FastFlowGS combines sparse matches, semi-dense tracks, and dense optical flow using a Kalman-style temporal update, achieving significant performance gains over existing baselines on both CMU-Panoptic and Monaco4D datasets. The benchmark provides varied illumination and viewpoints from trackside, onboard, and drone cameras, enabling evaluation of high-speed outdoor reconstruction.
STAR (Structure-aware Test-time Adaptation for diffusion-based light field Reconstruction) addresses the ill‑posed problem of reconstructing a light field from limited, noisy focal stack measurements by adapting a pre‑trained diffusion prior to each test scene. The method freezes the diffusion model and trains three lightweight adapters that jointly capture within‑view spatial details, cross‑view angular dependencies, and disparity across views. Experiments show that STAR surpasses state‑of‑the‑art approaches in both two‑ and three‑focal‑sheet settings while achieving faster inference times than methods that update parameters at test time.
arXiv:2608.29820v1 Announce Type: new Abstract: Ultrasound B-mode imaging commonly suffers from speckle noise and artifacts, requiring a delicate balance between contrast, resolution, and preservatio...
arXiv:2605. 00527v3 Announce Type: replace-cross Abstract: Lissajous confocal laser endomicroscopy (CLE) is a promising solution for high-speed in vivo optical biopsy for handheld scenarios.
arXiv:2609.07012v1 Announce Type: cross Abstract: Pushbroom satellite imaging couples limited spatial resolution with platform attitude instability. Platform jitter produces spatially varying motion...
The paper introduces a high‑resolution Rectified Flow Matching model trained on 256×256 chest CT images to serve as a generative prior for CT reconstruction. A two‑stage training strategy—initial strong anatomically informed augmentation followed by fine‑tuning—helps mitigate overfitting and improve structural fidelity. When evaluated on various CT inverse problems, Flow Matching‑based reconstruction methods outperform diffusion‑based algorithms in PSNR, SSIM, and perceptual quality while requiring fewer sampling steps.