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# Image Restoration

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  1. arXiv

    One network no longer handles all three CT degradations: AgentCT, a vision-language agent, routes each scan to a specialist and leads PSNR across nine settings

    Low-dose, sparse-view and metal-artifact CT degradations have different physics, yet universal restoration models have processed all of them with one shared architecture. AgentCT uses a LoRA-adapted Qwen3-VL-4B controller to identify degradation type and severity, dispatch the image to an image-domain diffusion denoiser or a dual-domain specialist, and re-check the restored output. On nine degradation-severity settings of patient-disjoint Mayo 2020 abdominal CT, routing with ground-truth degradation labels gave an overall PSNR/SSIM of 46.18 dB/0.984, versus 44.93 dB/0.976 for AMIR and 44.29 dB/0.979 for TAMP.

    Zhihao Chen（First author）
    AgentCTVision-language agentQwen3-VLImage Restoration
  2. arXiv

    One deblurring network for every camera: Fourier convolution learning lifts GoPro PSNR to 34.02 dB

    A unified motion deblurring network learns blur as a convolution by predicting input-adaptive filters in Fourier space, raising GoPro PSNR from the strong EVSSM baseline's 33.26 dB to 34.02 dB for 8G more FLOPs.

    East China Normal UniversityChinese Academy of Sciences
    Xintian Mao（First author）
    UniDeblurFourier Convolution LearningImage DeblurringImage Restoration
  3. arXiv

    Multi-modal fusion stops trading away semantics: an attack-defense game overlays a semantic map, lifting segmentation mIoU by 1.5

    Multi-modal image fusion has long traded visual quality against semantic attributes because the two optimization goals conflict. AD-SRF separates them: a transformer-based restorative visual fusion network builds a clear baseline, then an attack-defense game has an attacker mine semantic weaknesses while a defender generates an imperceptible semantic map overlaid back onto the fused image. On MFNet and FMB, it ranks first in four and five of six visual metrics, with segmentation mIoU 1.5 and 1.73 above the runner-up.

    Hao Zhang（First author）
    AD-SRFAttack-Defense GameRestorative Visual Fusion networkMulti-Modal Image FusionImage Restoration
  4. Nature Communications

    Live-cell nanoscopy stops burning out: tenfold less light, fourfold faster frames, and mitochondria caught budding

    Two deep-learning schemes extend RESOLFT live-cell nanoscopy to roughly five times longer recordings and four times higher frame rates, capturing 80 to 200 nanometer mitochondrial protrusions at 2 hertz.

    KTH Royal Institute of TechnologyTechnische Universität Dresden
    Guillaume Minet（First author）Ilaria Testa（Corresponding author）
    Hierarchical DivNoising (HDN)Fast-MoNaLISARESOLFT nanoscopyImage DenoisingImage Restoration
  5. arXiv

    Iris-3B pretrains a 3B pixel-space text-to-image model from scratch and finds no pixel-space prior advantage on depth or restoration

    The authors pretrain Iris-3B, a 3B-parameter pixel-space text-to-image transformer trained from scratch through a 256→512→1024 curriculum with v-prediction and DINOv2 REPA, and convert the latent FLUX.2 Klein base 4B to pixel space; after fine-tuning both pixel backbones for monocular depth and image restoration, they find no significant improvement from a pixel-space generative prior: on depth Iris-3B is level with latent FLUX.2 Klein while the converted model trails it, and on DIV2K restoration neither pixel model beats a latent FLUX.2 Klein fine-tune.

    Hanqiu Li Cai（First author）
    IRISText-to-Image GenerationLatent-to-Pixel ConversionDepth EstimationImage Restoration

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