28 citations · 71 across the 12 of their papers we have counts for
12 papers
Depth-Aware Super-Resolution via Distance-Adaptive Variational Formulation
Tianhao Guo, Bingjie Lu, Feng Wang +1
Single image super-resolution traditionally assumes spatially-invariant degradation models, yet real-world imaging systems exhibit complex distance-dependent effects including atmo…
CLIP-aware Domain-Adaptive Super-Resolution
Zhengyang Lu, Qian Xia, Weifan Wang +1
This work introduces CLIP-aware Domain-Adaptive Super-Resolution (CDASR), a novel framework that addresses the critical challenge of domain generalization in single image super-res…
Differentiable NMS via Sinkhorn Matching for End-to-End Fabric Defect Detection
Zhengyang Lu, Bingjie Lu, Weifan Wang +1
Fabric defect detection confronts two fundamental challenges. First, conventional non-maximum suppression disrupts gradient flow, which hinders genuine end-to-end learning. Second,…
CausalSR: Structural Causal Model-Driven Super-Resolution with Counterfactual Inference
Zhengyang Lu, Bingjie Lu, Feng Wang
Physical and optical factors interacting with sensor characteristics create complex image degradation patterns. Despite advances in deep learning-based super-resolution, existing m…
Single-image reflection removal via self-supervised diffusion models
Zhengyang Lu, Weifan Wang, Tianhao Guo +1
Reflections often degrade the visual quality of images captured through transparent surfaces, and reflection removal methods suffers from the shortage of paired real-world samples.…
Semi-supervised Chinese Poem-to-Painting Generation via Cycle-consistent Adversarial Networks
Zhengyang Lu, Tianhao Guo, Feng Wang
Classical Chinese poetry and painting represent the epitome of artistic expression, but the abstract and symbolic nature of their relationship poses a significant challenge for com…
Self-supervised Monocular Depth Estimation on Water Scenes via Specular Reflection Prior
Zhengyang Lu, Ying Chen
Monocular depth estimation from a single image is an ill-posed problem for computer vision due to insufficient reliable cues as the prior knowledge. Besides the inter-frame supervi…
City Scene Super-Resolution via Geometric Error Minimization
Zhengyang Lu, Feng Wang
Super-resolution techniques are crucial in improving image granularity, particularly in complex urban scenes, where preserving geometric structures is vital for data-informed cultu…
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects
Zhengyang Lu, Ying Chen
Significant attention has been attracted to deep learning-based depth estimates. Dynamic objects become the most hard problems in inter-frame-supervised depth estimates due to the…
Pyramid Frequency Network with Spatial Attention Residual Refinement Module for Monocular Depth Estimation
Zhengyang Lu, Ying Chen
Deep-learning-based approaches to depth estimation are rapidly advancing, offering superior performance over existing methods. To estimate the depth in real-world scenarios, depth…
Dense U-net for super-resolution with shuffle pooling layer
Zhengyang Lu, Ying Chen
Recent researches have achieved great progress on single image super-resolution(SISR) due to the development of deep learning in the field of computer vision. In these method, the…
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss
Zhengyang Lu, Ying Chen
Single image super-resolution (SISR) is the task of inferring a high-resolution image from a single low-resolution image. Recent research on super-resolution has achieved great pro…