arXiv subjects

Defeng He

Publications and source records attributed to Defeng He.

5 recordsLinked to original sources

arXiv2026

EPOFusion: Exposure aware Progressive Optimization Method for Infrared and Visible Image Fusion

Overexposure caused by strong daylight and oncoming headlights frequently overwhelms visible sensors, resulting in critical information loss in visual perception. Infrared and visible image fusion can compensate for such degradation via multimodal complementarity. However, most fusion methods lack region-aware optimization for overexposed areas and cannot effectively exploit infrared cues in saturated regions, resulting in insufficient infrared detail preservation or redundant information in the fused results. To address this, we propose EPOFusion, an exposure-aware fusion framework. It uses a spatial guidance module to selectively preserve informative infrared cues in overexposed regions. In addition, an iterative decoding head equipped with a multiscale context fusion module progressively refines fused representations, enabling effective infrared compensation in degraded regions while maintaining visual consistency in normal regions. The infrared and visible overexposure (IVOE) dataset is constructed with a synthetic training subset for controlled supervision and a real-world test subset for generalization assessment, supporting exposure-aware learning and evaluation. Extensive experiments on MSRS, FMB, and the proposed IVOE benchmark show that EPOFusion improves information preservation and visual fidelity, achieving an average full-image MI gain of 28.7% over the best competing methods. Qualitative results further demonstrate effective compensation in saturated regions, and downstream evaluations confirm its benefits under challenging overexposed conditions. Code, results, and the IVOE dataset will be made available at https://github.com/warren-wzw/EPOFusion.

cs.CV↗

arXiv2026

Joint Segmentation and Grading with Iterative Optimization for Multimodal Glaucoma Diagnosis

Accurate diagnosis of glaucoma is challenging, as early-stage changes are subtle and often lack clear structural or appearance cues. Most existing approaches rely on a single modality, such as fundus or optical coherence tomography (OCT), capturing only partial pathological information and often missing early disease progression. In this paper, we propose an iterative multimodal optimization model (IMO) for joint segmentation and grading. IMO integrates fundus and OCT features through a mid-level fusion strategy, enhanced by a cross-modal feature alignment (CMFA) module to reduce modality discrepancies. An iterative refinement decoder progressively optimizes the multimodal features through a denoising diffusion mechanism, enabling fine-grained segmentation of the optic disc and cup while supporting accurate glaucoma grading. Extensive experiments show that our method effectively integrates multimodal features, providing a comprehensive and clinically significant approach to glaucoma assessment. Source codes are available at https://github.com/warren-wzw/IMO.git.

cs.CV↗

arXiv2025

Varying Horizon Learning Economic MPC With Unknown Costs of Disturbed Nonlinear Systems

This paper proposes a novel varying horizon economic model predictive control (EMPC) scheme without terminal constraints for constrained nonlinear systems with additive disturbances and unknown economic costs. The general regression learning framework with mixed kernels is first used to reconstruct the unknown cost. Then an online iterative procedure is developed to adjust the horizon adaptively. Again, an elegant horizon-dependent contraction constraint is designed to ensure the convergence of the closed-loop system to a neighborhood of the desired steady state. Moreover, sufficient conditions ensuring recursive feasibility and input-to-state stability are established for the system in closed-loop with the EMPC. The merits of the proposed scheme are verified by the simulations of a continuous stirred tank reactor and a four-tank system in terms of robustness, economic performance and online computational burden.

eess.SY↗

arXiv2025

Convergence Filters for Efficient Economic MPC of Non-dissipative Systems

This note presents a novel and efficient Economic Model Predictive Control (EMPC) scheme specifically designed for non-dissipative systems subject to state and input constraints. To address the stability challenge of EMPC for constrained non-dissipative systems, a new concept of convergence filters is introduced. Three alternative convergence filters are designed accordingly to be incorporated into the receding horizon optimization problem of EMPC. To improve online computational efficiency, the variable horizon approach without explicit terminal state constraints is adopted. This design allows for a flexible trade-off among convergence speed, economic performance, and computational burden via simple parameter adjustment. Moreover, sufficient conditions are rigorously derived to guarantee recursive feasibility and stability. The advantages of the proposed EMPC are validated through simulations on a classical non-dissipative continuous stirred-tank reactor.

math.OC↗

arXiv2025

Learning Economic Model Predictive Control via Clustering and Kernel-Based Lipschitz Regression

This paper presents a novel learning economic model predictive control scheme for uncertain nonlinear systems subject to input and state constraints and unknown dynamics. We design a fast and accurate Lipschitz regression method using input and output data that combines clustering and kernel regression to learn the unknown dynamics. In each cluster, the parallel convex optimization problems are solved to estimate the kernel weights and reduce the Lipschitz constant of the predictor, hence limiting the error propagation in the prediction horizon. We derive the two different bounds of learning errors in deterministic and probabilistic forms and customize a new robust constraint-tightening strategy for the discontinuous predictor. Then, the learning economic model predictive control algorithm is formulated by introducing a stabilized optimization problem to construct a Lyapunov function. Sufficient conditions are derived to ensure the recursive feasibility and input-to-state stability of the closed-loop system. The effectiveness of the proposed algorithm is verified by simulations of a numerical example and a continuously stirred tank reactor.

eess.SY↗