arXiv subjects

Guibin Zan

Publications and source records attributed to Guibin Zan.

7 recordsLinked to original sources

arXiv2026

HazeSpikeMamba: Coupling Spiking-Inspired and State-Space Features for Self-Supervised Real-World Dehazing

Dehazing networks are commonly trained on synthetic hazy-clear pairs, but their performance often drops on real photographs. Synthetic haze generated using the atmospheric scattering model does not fully capture the variability of real haze, and paired real hazy-clear images are scarce. In this work, we propose HazeSpikeMamba, a compact dehazing framework that combines a spiking-inspired local path and an attentive state-space global path in a multi-scale U-Net. The local path uses TPCNNSpike, a new spike-emission scheme inspired by the neighborhood coupling of Pulse-Coupled Neural Network (PCNN). Unlike grouped directional scanning, TPCNNSpike updates all neurons in parallel using the previous firing states of their Gaussian-weighted neighborhoods. The global path adapts the Attentive State-Space Module of MambaIRv2, retaining semantic prompting and sequence reordering while removing the window self-attention branch. Its state-space processing models long-range dependencies with complexity linear in sequence length. For target-domain adaptation, a frozen degradation network, pretrained on paired NH-HAZE data, re-synthesizes haze from the dehazed prediction. The reconstruction error updates only the final restoration layers of HazeSpikeMamba without haze-free labels during adaptation. A shared checkpoint is adapted once on each complete unlabeled target set, making the evaluation dataset-level and transductive rather than zero-shot or per-image optimization. The forward network contains 2.02M active parameters and requires 13.27G nominal MACs (measured with thop at 256x256 input). This adaptation consistently improves BRISQUE and NIMA on RTTS, URHI, and HSTS. On RTTS, BRISQUE decreases from 30.13 to 27.72 and NIMA increases from 4.13 to 4.87. Under this transductive protocol, the adapted model also achieves the best BRISQUE and NIMA on URHI and HSTS among the compared methods.

cs.CV↗

arXiv2026

Ranking Image Fusion the Way Humans Do: A Learned Pairwise Preference Measure for Infrared-Visible Fusion Assessment

Infrared-visible image fusion (IVIF) has no ideal fused reference, so algorithms are ranked by scalar objective metrics that formalize proxies for information transfer, structure, or source similarity. These proxies often disagree with the judgment that ultimately matters: given the same sources, which of two fused results does a human prefer? Direct pairwise comparison is an established protocol for relative subjective assessment, but its cost grows quadratically with the number of algorithms. We present the Learned Perceptual Image Fusion Measure (LPIFM), a source-conditioned model that operationalizes the human A/B/Tie comparison protocol as a repeatable, scalable surrogate. LPIFM jointly observes the two sources and two fused candidates and predicts whether A is better, B is better, or the two are perceptually equivalent. Supervision comes from a new dense preference corpus covering all 6,300 unordered comparisons among 25 fusion methods on the 21 scenes of the VIFB benchmark, labeled under a blinded, randomized, two-stage protocol with expert adjudication. Across scene- and method-generalization settings, LPIFM attains pairwise accuracy of 0.792-0.840 and Spearman correlation of 0.941-0.977 with human-derived tie-aware Bradley-Terry rankings; on full 25-method pools it exceeds the strongest conventional metric by 0.163-0.211 in accuracy. Its verdicts are also antisymmetric under candidate swap, free of preference cycles, and fully transitive, matching or exceeding the internal consistency of the human panel. We publicly release the dataset, model weights, and code. LPIFM offers a practical instrument for human-aligned comparison and ranking of IVIF methods at scale.

cs.CV↗

arXiv2025

U-Net-Like Spiking Neural Networks for Single Image Dehazing

Image dehazing is a critical challenge in computer vision, essential for enhancing image clarity in hazy conditions. Traditional methods often rely on atmospheric scattering models, while recent deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Transformers, have improved performance by effectively analyzing image features. However, CNNs struggle with long-range dependencies, and Transformers demand significant computational resources. To address these limitations, we propose DehazeSNN, an innovative architecture that integrates a U-Net-like design with Spiking Neural Networks (SNNs). DehazeSNN captures multi-scale image features while efficiently managing local and long-range dependencies. The introduction of the Orthogonal Leaky-Integrate-and-Fire Block (OLIFBlock) enhances cross-channel communication, resulting in superior dehazing performance with reduced computational burden. Our extensive experiments show that DehazeSNN is highly competitive to state-of-the-art methods on benchmark datasets, delivering high-quality haze-free images with a smaller model size and less multiply-accumulate operations. The proposed dehazing method is publicly available at https://github.com/HaoranLiu507/DehazeSNN.

cs.CV↗

arXiv2024

Data-driven imaging geometric recovery of ultrahigh resolution robotic micro-CT for in-vivo and other applications

We introduce an ultrahigh-resolution (50μm\) robotic micro-CT design for localized imaging of carotid plaques using robotic arms, cutting-edge detector, and machine learning technologies. To combat geometric error-induced artifacts in interior CT scans, we propose a data-driven geometry estimation method that maximizes the consistency between projection data and the reprojection counterparts of a reconstructed volume. Particularly, we use a normalized cross correlation metric to overcome the projection truncation effect. Our approach is validated on a robotic CT scan of a sacrificed mouse and a micro-CT phantom scan, both producing sharper images with finer details than that prior correction.

physics.med-ph↗

arXiv2022

Subspace modeling for fast and high-sensitivity X-ray chemical imaging

Resolving morphological chemical phase transformations at the nanoscale is of vital importance to many scientific and industrial applications across various disciplines. The TXM-XANES imaging technique, by combining full field transmission X-ray microscopy (TXM) and X-ray absorption near edge structure (XANES), has been an emerging tool which operates by acquiring a series of microscopy images with multi-energy X-rays and fitting to obtain the chemical map. Its capability, however, is limited by the poor signal-to-noise ratios due to the system errors and low exposure illuminations for fast acquisition. In this work, by exploiting the intrinsic properties and subspace modeling of the TXM-XANES imaging data, we introduce a simple and robust denoising approach to improve the image quality, which enables fast and high-sensitivity chemical imaging. Extensive experiments on both synthetic and real datasets demonstrate the superior performance of the proposed method.

eess.IV↗

arXiv2019

Preliminary demonstration of flexible dual-energy X-ray phase-contrast imaging

Currently, dual-energy X-ray phase contrast imaging is usually conducted with an X-ray Talbot-Lau interferometer. However, in this system, the two adopted energy spectra have to be chosen carefully in order to match well with the phase grating. For example, the accelerating voltages of the X-ray tube are supposed to be respectively set as 40 kV and 70 kV, with other energy spectra being practically unusable for dual energy imaging. This system thus has low flexibility and maneuverability in practical applications. In this work, dual energy X-ray phase-contrast imaging is performed in a grating-based non-interferometric imaging system rather than in a Talbot-Lau interferometer. The advantage of this system is that, theoretically speaking, any two separated energy spectra can be utilized to perform dual energy X-ray phase-contrast imaging. The preliminary experimental results show that dual-energy X-ray phase contrast imaging is successfully performed when the accelerating voltages of the X-ray tube are successively set as 40 kV and 50 kV. Our work increases the flexibility and maneuverability when employing dual-energy X-ray phase-contrast imaging in medical diagnoses and nondestructive tests.

physics.med-ph↗

arXiv2016

Toward a fractal spectrum approach for neutron and gamma pulse shape discrimination

There is a key research issue to accurately select out neutron signals and discriminate gamma signals from a mixed radiation field in the neutron detection. This paper proposes a fractal spectrum discrimination approach by means of different spectrum characteristics of neutron and gamma. Figure of merit and average discriminant error ratio are adopted together to evaluate the discriminant effects. Different neutron and gamma signals with various noises and pulse pile-ups are simulated according to real data in the literature. The proposed approach is compared with the digital charge integration and pulse gradient methods. It is found that the fractal approach exhibits the best discriminant performance among three methods. The fractal spectrum approach is not sensitive to the high frequency noises and pulse pile-ups. It means that the proposed approach takes the advantages of anti-noises and high discriminant ability, and can be used to better discriminate neutron and gamma in neutron detection.

physics.ins-det↗