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cs.CV2026
Does Your ViT Still Need U-Net for Segmentation?
Xin Li, Wenhui Zhu, Xuanzhao Dong +6
Medical image segmentation is dominated by U-Net-style encoder-decoder architectures. Vision Transformers (ViTs) overcome the limited receptive field of convolutional networks thro…
cs.CV2026
Bridging Restoration and Diagnosis: A Comprehensive Benchmark for Retinal Fundus Enhancement
Xuanzhao Dong, Wenhui Zhu, Xiwen Chen +8
Over the past decade, generative models have demonstrated success in enhancing fundus images. However, the evaluation of these models remains a challenge. A benchmark for fundus im…
cs.CV2026
SGW-GAN: Sliced Gromov-Wasserstein Guided GANs for Retinal Fundus Image Enhancement
Yujian Xiong, Xuanzhao Dong, Wenhui Zhu +3
Retinal fundus photography is indispensable for ophthalmic screening and diagnosis, yet image quality is often degraded by noise, artifacts, and uneven illumination. Recent GAN- an…