The paper introduces a two‑stage, projection‑based framework for reconstructing images from only Fourier phase information, using an image‑specific neural prior alongside Fourier‑phase and spatial‑support constraints. In the first stage, the method alternates between enforcing constraints and updating the neural prior, while the second stage refines phase and support with guaranteed convergence. Experiments on 77 microscopy images show that the best variant achieves a pooled PSNR of 31.41 dB, a mean PSNR of 35.75 dB, and a mean SSIM of 0.9531, improving pooled PSNR by 1.51 dB and reducing pooled MSE by 29.3% compared to a constraint‑only baseline.
arXiv:2609.13969v1 Announce Type: new Abstract: Ptychographic phase reconstruction is commonly formulated as an iterative inverse problem, requiring repeated object-probe updates and resulting in sub...
MG-SpaIR is a training-data-free framework for restoring a clean image from a single observation corrupted by a mixture of blur, downsampling, noise, and missing pixels. Building on implicit neural representations (INRs), we introduce a multi-grade coarse-to-fine residual hierarchy that progressively refines the reconstruction across resolution grades, improving representational fidelity and mitigating spectral limitations.
arXiv:2608. 02869v1 Announce Type: new Abstract: Ptychography neural networks suffer from scaling inconsistencies when generalizing out of distribution, limiting their real world viability.
arXiv:2608. 15647v1 Announce Type: cross Abstract: Semantic segmentation of very-high-resolution (VHR) remote sensing imagery increasingly benefits from strong pretrained hierarchical encoders, yet exploiting their multi-stage representations remains difficult.
arXiv:2505.05855v4 Announce Type: replace Abstract: Multi-contrast super-resolution (MCSR) is crucial for enhancing MRI but current deep learning methods are limited. They typically require large, pa...
arXiv:2508. 05321v4 Announce Type: replace-cross Abstract: Assume you encounter an inverse problem that shall be solved for a large number of data, but no ground-truth data is available.
arXiv:2606. 07196v1 Announce Type: new Abstract: Classical sparse Type-II Bayesian methods for M/EEG brain imaging support joint estimation of source and noise hyperparameters, but rely on fixed iterative update rules.
arXiv:2609. 20562v1 Announce Type: new Abstract: Automated quality assessment, enhancement, and segmentation of multiple structures in $0.
arXiv:2605.24625v2 Announce Type: replace Abstract: Ultra-low-field (ULF) MRI offers portable and accessible neuroimaging but suffers from reduced signal-to-noise ratio and limited spatial resolution...
Consist‑Retinex introduces a one‑step noise‑emphasized consistency training framework for Retinex‑based low‑light image enhancement. It first decomposes images into reflectance and illumination maps using a Retinex Transformer Decomposition Network, then trains two conditional consistency models with a dual objective that blends trajectory consistency and ground‑truth alignment. The method employs adaptive noise‑emphasized fixed‑point sampling to focus supervision near the inference endpoint, achieving state‑of‑the‑art VE‑LOL‑L scores on paired and unpaired low‑light benchmarks while reducing sampling and training costs.
arXiv:2608. 16546v1 Announce Type: cross Abstract: Most super-resolution models learn from paired data by supervising only the final high-resolution output.
The paper introduces CoSMo-RecNet, a modular framework for multi-contrast MRI reconstruction that operates effectively in low-data regimes. It leverages a reusable content/style prior learned from large, unpaired multi-contrast image datasets, allowing a lightweight unrolled network to refine reconstructions using only a few task‑specific training samples. Experiments on low‑field 0.3 T and ultra‑low‑field 47 mT datasets demonstrate that CoSMo-RecNet outperforms parameter‑matched MoDL, classical reconstruction, transfer learning, and zero‑shot methods, achieving higher quality with as few as five training subjects.