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- Jiangtao CuiSuggested from co-authorship
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Works197 from public data
- An overview of multi-modal medical image fusion368
In this review, methods in the field of medical image fusion are characterized by image decomposition and image reconstruction, image fusion rules, image quality assessments, and experiments on the benchmark dataset.
- Pixel convolutional neural network for multi-focus image fusion298
Experimental results demonstrate that the proposed p-CNN is competitive with or even outperforms the state-of-the-art methods in terms of both subjective visual perception and objective evaluation metrics.
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- Union Laplacian pyramid with multiple features for medical image fusion229
Visual and statistical analyses show that the quality of fused image can be significantly improved over that of typical image quality assessment metrics in terms of structural similarity, peak-signal-to-noise ratio, standard deviation, and tone mapped image quality index metrics.
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- Detecting Generated Images by Real Images178
This work observed that the noise pattern of real images exhibits similar characteristics in the frequency domain, while the generated images are far different, and can perform image authentication by checking whether an image follows the patterns of authentic images.
- Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption175
A block-stacked style architecture with clique blocks is employed, and in each clique block a bidirectional connection structure is introduced in the proposed CNN, which achieves both spatial and channel attention leading a promising classification performance.
- Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering161
The proposed C2RNet cascades a coarse convolutional neural network and a refined CNN and extracts the differences in the image properties between un-tampered and tampered regions from image patches with different scales to guarantee a stable detection performance.
- TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance149
The method introduces two core techniques: affinity mimicking and weight inheritance, which enables student models to mimic teachers’ behavior of learning cross-modal feature alignment in a visual-linguistic affinity space during distillation.
- Computer aided Alzheimer's disease diagnosis by an unsupervised deep learning technology149
This paper tackled the problem of automatic prediction of Alzheimer's Disease based on Magnetic Resonance Imaging (MRI) images, and proposed a fully unsupervised deep learning technology for AD diagnosis, which first implement the un Supervised Convolutional Neural Networks (CNNs) for feature extraction, and then utilize the un supervised predictor to achieve the final diagnosis.
- Image analysis by Bessel–Fourier moments143
A new set of moments based on the Bessel function of the first kind, named Bessel-Fourier moments (BFMs), which are more suitable than orthogonal Fourier-Mellin and Zernike moments for image analysis and rotation invariant pattern recognition.
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- Image analysis by fractional-order orthogonal moments115
Fractional-order orthogonal moments are not only capable of region-of-interest (ROI) feature extraction but also have potential for image reconstruction and face recognition and have high noise robustness in invariant image recognition.
- Fractional discrete Tchebyshev moments and their applications in image encryption and watermarking104
A novel framework for deriving fractional order DTMs (FrDTMs) by the eigen-decomposition of kernel matrices is proposed in this paper, and some properties of the proposed FrDTMs are analyzed.
- Fast Continual Multi-View Clustering With Incomplete Views93
Comprehensive experiments conducted on various datasets demonstrate the superiority of FCMVC-IV over the competing approaches, which maintains a scalable consensus coefficient matrix and updates its knowledge with the incoming incomplete view rather than storing and recomputing all the data matrices.
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- Scaling and rotation invariant analysis approach to object recognition based on Radon and Fourier–Mellin transforms87
Results show the high classification accuracy of this approach as a result of using the rotation and scaling invariant function instead of image binarization and normalization, it is also shown that this method is relatively robust in the presence of white noise.
- Brightness and contrast controllable image enhancement based on histogram specification84
Two novel histogram based image enhancement algorithms are proposed that provide the way to control the brightness and contrast of enhanced image by adjusting two parameters.
- Recent Advances for Aerial Object Detection: A Survey82
This survey presents a comprehensive review of recent advances in aerial object detection and summarizes the five imbalance problems of aerial object detection, including scale imbalance, spatial imbalance, objective imbalance, semantic imbalance, and class imbalance.
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- Lossless image compression based on integer Discrete Tchebichef Transform77
Results show that the proposed iDTT algorithm not only has higher compression ratio than iDCT method, but also is compatible with the widely used JPEG standard, and a framework of lossless image compression based on integer DTT is proposed.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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