298 citations · 1.5k across the 72 of their papers we have counts for
10 papers · 1 filter
Practical Lossless Volumetric Medical Image Compression via Tri-plane Context Tree Learning
Yuanchao Bai, Yifan Zhao, Kai Wang +5
Lossless compression of volumetric medical images is of paramount importance for clinical and research applications where data fidelity is essential. Traditional compression method…
Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural Image Compression
Daxin Li, Yuanchao Bai, Kai Wang +2
Recent advancements in neural compression have surpassed traditional codecs in PSNR and MS-SSIM measurements. However, at low bit-rates, these methods can introduce visually disple…
Retinex-based Image Denoising / Contrast Enhancement using Gradient Graph Laplacian Regularizer
Yeganeh Gharedaghi, Gene Cheung, Xianming Liu
Images captured in poorly lit conditions are often corrupted by acquisition noise. Leveraging recent advances in graph-based regularization, we propose a fast Retinex-based restora…
Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image Compression
Yuanchao Bai, Xianming Liu, Kai Wang +3
Lossless and near-lossless image compression is of paramount importance to professional users in many technical fields, such as medicine, remote sensing, precision engineering and…
Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution
Qing Ma, Junjun Jiang, Xianming Liu +1
To solve the ill-posed problem of hyperspectral image super-resolution (HSISR), an usually method is to use the prior information of the hyperspectral images (HSIs) as a regulariza…
BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring
Pengwei Liang, Junjun Jiang, Xianming Liu +1
The defocus deblurring raised from the finite aperture size and exposure time is an essential problem in the computational photography. It is very challenging because the blur kern…
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Grigory Malivenko, David Plowman +35
Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually…
Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery
Junjun Jiang, He Sun, Xianming Liu +1
Recently, single gray/RGB image super-resolution reconstruction task has been extensively studied and made significant progress by leveraging the advanced machine learning techniqu…
Rapid Whole Slide Imaging via Learning-based Two-shot Virtual Autofocusing
Qiang Li, Xianming Liu, Kaige Han +3
Whole slide imaging (WSI) is an emerging technology for digital pathology. The process of autofocusing is the main influence of the performance of WSI. Traditional autofocusing met…
ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising
Yongsen Zhao, Deming Zhai, Junjun Jiang +1
Hyperspectral image (HSI) denoising is of crucial importance for many subsequent applications, such as HSI classification and interpretation. In this paper, we propose an attention…