Bingjie Lu

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 methods overlook the causal nature of degradation by adopting simplistic black-box mappings. This paper formulates super-resolution using structural causal models to reason about image degradation processes. We establish a mathematical foundation that unifies principles from causal inference, deriving necessary conditions for identifying latent degradation mechanisms and corresponding propagation. We propose a novel counterfactual learning strategy that leverages semantic guidance to reason about hypothetical degradation scenarios, leading to theoretically-grounded representations that capture invariant features across different degradation conditions. The framework incorporates an adaptive intervention mechanism with provable bounds on treatment effects, allowing precise manipulation of degradation factors while maintaining semantic consistency. Through extensive empirical validation, we demonstrate that our approach achieves significant improvements over state-of-the-art methods, particularly in challenging scenarios with compound degradations. On standard benchmarks, our method consistently outperforms existing approaches by significant margins (0.86-1.21dB PSNR), while providing interpretable insights into the restoration process. The theoretical framework and empirical results demonstrate the fundamental importance of causal reasoning in understanding image restoration systems.

Bingjie Lu, Han-Cheng Dan, Yichen Zhang et al.

Mean texture depth (MTD) is pivotal in assessing the skid resistance of asphalt pavements and ensuring road safety. This study focuses on developing an automated system for extracting texture features and evaluating MTD based on pavement images. The contributions of this work are threefold: firstly, it proposes an economical method to acquire three-dimensional (3D) pavement texture data; secondly, it enhances 3D image processing techniques and formulates features that represent various aspects of texture; thirdly, it establishes multivariate prediction models that link these features with MTD values. Validation results demonstrate that the Gradient Boosting Tree (GBT) model achieves remarkable prediction stability and accuracy (R2 = 0.9858), and field tests indicate the superiority of the proposed method over other techniques, with relative errors below 10%. This method offers a comprehensive end-to-end solution for pavement quality evaluation, from images input to MTD predictions output.

Bingjie Lu, Zhengyang Lu, Yijiashun Qi et al.

Pavement skid resistance is of vital importance for road safety. The objective of this study is to propose and validate a texture-based image indicator to predict pavement friction. This index enables pavement friction to be measured easily and inexpensively using digital images. Three different types of asphalt surfaces (dense-graded asphalt mix, open-grade friction course, and chip seal) were evaluated subject to various tire polishing cycles. Images were taken with corresponding friction measured using Dynamic Friction Tester (DFT) in the laboratory. The aggregate protrusion area is proposed as the indicator. Statistical models are established for each asphalt surface type to correlate the proposed indicator with friction coefficients. The results show that the adjusted R-square values of all relationships are above 0.90. Compared to other image-based indicators in the literature, the proposed image indicator more accurately reflects the changes in pavement friction with the number of polishing cycles, proving its cost-effective use for considering pavement friction in mix design stage.

Tianhao Guo, Bingjie Lu, Feng Wang et al.

Single image super-resolution traditionally assumes spatially-invariant degradation models, yet real-world imaging systems exhibit complex distance-dependent effects including atmospheric scattering, depth-of-field variations, and perspective distortions. This fundamental limitation necessitates spatially-adaptive reconstruction strategies that explicitly incorporate geometric scene understanding for optimal performance. We propose a rigorous variational framework that characterizes super-resolution as a spatially-varying inverse problem, formulating the degradation operator as a pseudodifferential operator with distance-dependent spectral characteristics that enable theoretical analysis of reconstruction limits across depth ranges. Our neural architecture implements discrete gradient flow dynamics through cascaded residual blocks with depth-conditional convolution kernels, ensuring convergence to stationary points of the theoretical energy functional while incorporating learned distance-adaptive regularization terms that dynamically adjust smoothness constraints based on local geometric structure. Spectral constraints derived from atmospheric scattering theory prevent bandwidth violations and noise amplification in far-field regions, while adaptive kernel generation networks learn continuous mappings from depth to reconstruction filters. Comprehensive evaluation across five benchmark datasets demonstrates state-of-the-art performance, achieving 36.89/0.9516 and 30.54/0.8721 PSNR/SSIM at 2 and 4 scales on KITTI outdoor scenes, outperforming existing methods by 0.44dB and 0.36dB respectively. This work establishes the first theoretically-grounded distance-adaptive super-resolution framework and demonstrates significant improvements on depth-variant scenarios while maintaining competitive performance across traditional benchmarks.

Bing-Ze Lu, Richard Tsai

In many applications, one needs to learn a dynamical system from its solutions sampled at a finite number of time points. The learning problem is often formulated as an optimization problem over a chosen function class. However, in the optimization procedure, it is necessary to employ a numerical scheme to integrate candidate dynamical systems and assess how their solutions fit the data. This paper reveals potentially serious effects of a chosen numerical scheme on the learning outcome. In particular, our analysis demonstrates that a damped oscillatory system may be incorrectly identified as having "anti-damping" and exhibiting a reversed oscillation direction, despite adequately fitting the given data points.