Search Results for author:
Found 16 papers, 8 papers with code
RefQSR: Reference-based Quantization for Image Super-Resolution Networks
Single image super-resolution (SISR) aims to reconstruct a high-resolution image from its low-resolution observation.
Spectrum Translation for Refinement of Image Generation (STIG) Based on Contrastive Learning and Spectral Filter Profile
We evaluate our framework across eight fake image datasets and various cutting-edge models to demonstrate the effectiveness of STIG.
A Unified Multi-Phase CT Synthesis and Classification Framework for Kidney Cancer Diagnosis with Incomplete Data
In this paper, we propose a unified framework for kidney cancer diagnosis with incomplete multi-phase CT, which simultaneously recovers missing CT images and classifies cancer subtypes using the completed set of images.
Exploring 3D U-Net Training Configurations and Post-Processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge
In 2023, it is estimated that 81, 800 kidney cancer cases will be newly diagnosed, and 14, 890 people will die from this cancer in the United States.
Hierarchical Spatiotemporal Transformers for Video Object Segmentation
This paper presents a novel framework called HST for semi-supervised video object segmentation (VOS).
Multi-frame-based Cross-domain Denoising for Low-dose Spiral Computed Tomography
Computed tomography (CT) has been used worldwide as a non-invasive test in assisting diagnosis.
MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation
We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting.
Video Object Segmentation-aware Video Frame Interpolation
In this paper, we propose a video object segmentation (VOS)-aware training framework called VOS-VFI that allows VFI models to interpolate frames with more precise object boundaries.
RZSR: Reference-based Zero-Shot Super-Resolution with Depth Guided Self-Exemplars
To advance ZSSR, we obtain reference image patches with rich textures and high-frequency details which are also extracted only from the input image using cross-scale matching.
RAWtoBit: A Fully End-to-end Camera ISP Network
In this paper, we investigate the designing of a fully end-to-end optimized camera ISP incorporating image compression.
GPS-GLASS: Learning Nighttime Semantic Segmentation Using Daytime Video and GPS data
Nighttime semantic segmentation is especially challenging due to a lack of annotated nighttime images and a large domain gap from daytime images with sufficient annotation.
Ranked #8 on
Semantic Segmentation
on Dark Zurich
XYDeblur: Divide and Conquer for Single Image Deblurring
Many convolutional neural networks (CNNs) for single image deblurring employ a U-Net structure to estimate latent sharp images.
Rethinking Coarse-to-Fine Approach in Single Image Deblurring
Coarse-to-fine strategies have been extensively used for the architecture design of single image deblurring networks.
Ranked #5 on
Deblurring
on RSBlur
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention
In addition, we propose an improved information aggregation module with PAKA, called the hierarchical PAKA module (HPM).
Progressive Joint Low-light Enhancement and Noise Removal for Raw Images
Low-light imaging on mobile devices is typically challenging due to insufficient incident light coming through the relatively small aperture, resulting in a low signal-to-noise ratio.
GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling
In this paper, we propose a grouped residual dense network (GRDN), which is an extended and generalized architecture of the state-of-the-art residual dense network (RDN).
