40 citations · 111 across the 14 of their papers we have counts for
14 papers
DiffI2I: Efficient Diffusion Model for Image-to-Image Translation
Bin Xia, Yulun Zhang, Shiyin Wang +6
The Diffusion Model (DM) has emerged as the SOTA approach for image synthesis. However, the existing DM cannot perform well on some image-to-image translation (I2I) tasks. Differen…
Hierarchical Integration Diffusion Model for Realistic Image Deblurring
Zheng Chen, Yulun Zhang, Ding Liu +4
Diffusion models (DMs) have recently been introduced in image deblurring and exhibited promising performance, particularly in terms of details reconstruction. However, the diffusio…
DiffIR: Efficient Diffusion Model for Image Restoration
Bin Xia, Yulun Zhang, Shiyin Wang +5
Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis process into a sequential application of a denoising network. However, different from image synth…
Weakly Supervised Image Segmentation Beyond Tight Bounding Box Annotations
Juan Wang, Bin Xia
Weakly supervised image segmentation approaches in the literature usually achieve high segmentation performance using tight bounding box supervision and decrease the performance gr…
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…
Basic Binary Convolution Unit for Binarized Image Restoration Network
Bin Xia, Yulun Zhang, Yitong Wang +4
Lighter and faster image restoration (IR) models are crucial for the deployment on resource-limited devices. Binary neural network (BNN), one of the most promising model compressio…
Meta-Learning based Degradation Representation for Blind Super-Resolution
Bin Xia, Yapeng Tian, Yulun Zhang +3
The most of CNN based super-resolution (SR) methods assume that the degradation is known (\eg, bicubic). These methods will suffer a severe performance drop when the degradation is…
Structured Sparsity Learning for Efficient Video Super-Resolution
Bin Xia, Jingwen He, Yulun Zhang +4
The high computational costs of video super-resolution (VSR) models hinder their deployment on resource-limited devices, (e.g., smartphones and drones). Existing VSR models contain…
SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization
Yucheng Hang, Bin Xia, Wenming Yang +1
Image harmonization aims to achieve visual consistency in composite images by adapting a foreground to make it compatible with a background. However, existing methods always only u…
Polar Transformation Based Multiple Instance Learning Assisting Weakly Supervised Image Segmentation With Loose Bounding Box Annotations
Juan Wang, Bin Xia
This study investigates weakly supervised image segmentation using loose bounding box supervision. It presents a multiple instance learning strategy based on polar transformation t…
Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-Resolution
Bin Xia, Yapeng Tian, Yucheng Hang +3
Reference-based super-resolution (RefSR) has made significant progress in producing realistic textures using an external reference (Ref) image. However, existing RefSR methods obta…
Efficient Non-Local Contrastive Attention for Image Super-Resolution
Bin Xia, Yucheng Hang, Yapeng Tian +3
Non-Local Attention (NLA) brings significant improvement for Single Image Super-Resolution (SISR) by leveraging intrinsic feature correlation in natural images. However, NLA gives…
Bounding Box Tightness Prior for Weakly Supervised Image Segmentation
Juan Wang, Bin Xia
This paper presents a weakly supervised image segmentation method that adopts tight bounding box annotations. It proposes generalized multiple instance learning (MIL) and smooth ma…
CDRNet: Accurate Cup-to-Disc Ratio Measurement with Tight Bounding Box Supervision in Fundus Photography Using Deep Learning
Juan Wang, Bin Xia
The cup-to-disc ratio (CDR) is one of the most significant indicator for glaucoma diagnosis. Different from the use of costly fully supervised learning formulation with pixel-wise…