CEM‑TUDASR is a lightweight, unsupervised Transformer‑based super‑resolution framework designed for Wireless Capsule Endoscopy (WCE) images. It uses a domain‑adaptive degradation network to synthesize realistic low‑resolution WCE images from high‑resolution conventional endoscopy data, enabling unpaired training. The SR generator incorporates Deep Attention Blocks and a Fusion Attention Block to preserve both global context and fine local structures, achieving superior no‑reference quality metrics and improved restoration of mucosal textures, vascular patterns, and anatomical details while remaining computationally efficient.
By Anjali Sarvaiya, Jay Kadel, Kishor Upla, Kiran Raja
CEM‑TUDASR is a lightweight, unsupervised Transformer-based super‑resolution framework designed to enhance low‑resolution images from Wireless Capsule Endoscopy (WCE). It uses a domain‑adaptive degradation network to generate realistic WCE‑like low‑resolution images from high‑resolution conventional endoscopy data, enabling effective unpaired learning. The model incorporates Deep Attention Blocks and a Fusion Attention Block to capture both global context and fine local details, achieving superior performance on WCE datasets and demonstrating cross‑domain adaptability to retinal images, all while keeping the parameter count and computational load low.
EndoFSA is a GAN-based model designed for endoscopic few-shot image generation, addressing the scarcity of pathological samples in wireless capsule endoscopy (WCE) data. It adapts a generator pretrained on abundant normal images to abnormal domains by updating only a small set of rank-constrained modulation parameters while keeping the rest of the weights frozen, thereby preserving anatomical priors and preventing mode collapse. The method incorporates perceptual boundary regularization and cluster-wise diversity control, operates without pixel-level annotations, and demonstrates that synthetic abnormal images can match real images in downstream classification performance.
By Panagiota Gatoula, Grigoris Karypidis, Dimitris K. Iakovidis
arXiv:2404. 06294v2 Announce Type: replace-cross Abstract: Super-Resolution (SR) is a time-hallowed image processing problem that aims to improve the quality of a Low-Resolution (LR) sample up to the standard of its High-Resolution (HR) counterpart.
By Arkaprabha Basu, Kushal Bose, Sankha Subhra Mullick, Anish Chakrabarty, Swagatam Das
TransNSNet is a Transformer‑enhanced multi‑scale adversarial framework designed for CT‑to‑PET image synthesis. The study curated a paired CT‑PET dataset from a public NSCLC Radiogenomics collection and evaluated the framework against several baselines, reporting that TransNSNet achieved the best mean performance in nine of ten metrics while maintaining a smaller parameter footprint. Statistical analysis found no significant superiority over other models after correction, and the authors caution that synthesized PET‑like images should not replace clinically acquired PET scans.
By Shaojun Li, Xiaoyu Deng, Ikuo Keshi
arXiv:2608.22619v1 Announce Type: cross
Abstract: Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-t...
By Md Maklachur Rahman, Md Hasan Al Banna, Saraf Anjum, Mahmudul Hasan, Tracy Hammond
PanoDiff-SR is a two‑stage pipeline that generates full‑size dental panoramic radiographs (PRs) at diagnostic resolution. First, a denoising diffusion implicit model creates a low‑resolution PR seed, which is then upscaled by a fine‑tuned hybrid attention transformer to a high‑resolution image. The method achieves realistic results, with a Fréchet inception distance of 40.5 and a clinician detection accuracy of 68.5%, closer to chance than previous GAN‑based approaches.
By Sanyam Jain, Bruna Neves de Freitas, Andreas Basse-OConnor, Alexandros Iosifidis, Ruben Pauwels
arXiv:2608. 03990v1 Announce Type: new Abstract: Synthetic histopathology image generation has emerged as an approach that may address data scarcity in computational pathology, yet current evaluation methodologies may not fully assess synthetic data quality for medical applications.
By Seyed Kahaki, Shijie Li, Weijie Chen, Nicholas Petrick
arXiv:2609.15120v1 Announce Type: new
Abstract: Benefiting from the powerful generative priors of diffusion models, diffusion-based real-world image super-resolution (Real-ISR) methods have demonstra...
By Shuhao Han, Wenjie Liao, Hayden Vance, Hang Dong, Rui Zhang, Chun-Le Guo, Chongyi Li
arXiv:2204. 14224v3 Announce Type: replace-cross Abstract: The automated analysis of heterogeneous natural textures is frequently hindered by physical damage and data loss, presenting a significant challenge to computer vision.
By Galymzhan Abdimanap, Kairat Bostanbekov, Abdelrahman Abdallah, Anel Alimova, Darkhan Kurmangaliyev, Daniyar Nurseitov, Tatyana Dedova, Larissa Balakay, Serik Nurakynov
arXiv:2607. 15711v1 Announce Type: cross Abstract: Diffusion-based methods have achieved impressive performance in real-world image super-resolution (Real-ISR) by leveraging large pre-trained stable diffusion (SD) models as powerful generative priors.
By Xue Wu, Kang Zhao, Kafeng Wang, Jianfei Chen, Jingwei Xin, Nannan Wang, Xinbo Gao
Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model. Most recent methods adapt large pretrained text-to-image (T2I) latent diffusion models for their strong capacity and generative priors.