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Perspective+ Unet: Enhancing Segmentation with Bi-Path Fusion and Efficient Non-Local Attention for Superior Receptive Fields
Jintong Hu, Siyan Chen, Zhiyi Pan +2
Precise segmentation of medical images is fundamental for extracting critical clinical information, which plays a pivotal role in enhancing the accuracy of diagnoses, formulating e…
Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
Yue Lv, Jinxi Xiang, Jun Zhang +3
The latest advancements in neural image compression show great potential in surpassing the rate-distortion performance of conventional standard codecs. Nevertheless, there exists a…
Dual Arbitrary Scale Super-Resolution for Multi-Contrast MRI
Jiamiao Zhang, Yichen Chi, Jun Lyu +2
Limited by imaging systems, the reconstruction of Magnetic Resonance Imaging (MRI) images from partial measurement is essential to medical imaging research. Benefiting from the div…
Knowledge Distillation based Degradation Estimation for Blind Super-Resolution
Bin Xia, Yulun Zhang, Yitong Wang +4
Blind image super-resolution (Blind-SR) aims to recover a high-resolution (HR) image from its corresponding low-resolution (LR) input image with unknown degradations. Most of the e…
Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images
Jinjin Gu, Haoming Cai, Chenyu Dong +4
Rendering high-resolution (HR) graphics brings substantial computational costs. Efficient graphics super-resolution (SR) methods may achieve HR rendering with small computing resou…
RFormer: Transformer-based Generative Adversarial Network for Real Fundus Image Restoration on A New Clinical Benchmark
Zhuo Deng, Yuanhao Cai, Lu Chen +6
Ophthalmologists have used fundus images to screen and diagnose eye diseases. However, different equipments and ophthalmologists pose large variations to the quality of fundus imag…