AIMC Topic: Wavelet Analysis

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YOLOv11-WBD: A wavelet-bidirectional network with dilated perception for robust metal surface defect detection.

PloS one
In the field of quality control, metal surface defect detection is an important yet challenging task. Although YOLO models perform well in most object detection scenarios, metal surface images under operational conditions often exhibit coexisting hig...

HWANet: A Haar Wavelet-based Attention Network for remote sensing object detection.

PloS one
Remote sensing object detection (RSOD) is highly challenging due to large variations in object scales. Existing deep learning-based methods still face limitations in addressing this challenge. Specifically, reliance on stride convolutions during down...

Hyperspectral anomaly detection leveraging spatial attention and right-shifted spectral energy.

PloS one
In this research, we have proposed a novel anomaly detection algorithm for processing hyperspectral images (HSIs), called the Graph Attention Network-Beta Wavelet Graph Neural Network-based Hyperspectral Anomaly Detection (GAN-BWGNN HAD). This algori...

Lightweight wavelet convolutional network for guidewire segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate guidewire segmentation is crucial for the success of vascular interventional procedures. Existing methods rely on a large number of parameters, making it difficult to balance performance and model size. In addition, the difficulty of collect...

SNA-SKAN: Unpaired learning for SDOCT speckle noise removal based on self noise assist and kolmogorov-arnold network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Optical Coherence Tomography (OCT) will inevitably be contaminated by speckle noise when imaging, resulting in a decrease in the visual quality of images and affecting clinical diagnosis. Existing unsupervised denoising methods often rely on complex ...

Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks.

Scientific reports
Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inher...

Enhancing breast cancer classification using a deep sparse wavelet autoencoder approach.

Scientific reports
As digital imaging technology advances, accurate classification of 2D breast cancer images becomes increasingly crucial for early detection and staging. This paper introduces a novel classification approach that integrates deep learning, sparse codin...

Frequency domain manipulation of multiple copy-move forgery in digital image forensics.

PloS one
Copy move forgery is a type of image forgery in which a portion of the original image is copied and pasted in a new location on the same image. The consistent illumination and noise pattern make this kind of forgery more difficult to detect. In copy-...

Motor imagery EEG signal classification using novel deep learning algorithm.

Scientific reports
Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurological disorder detection and cognitive state monitoring. However, these technologies face challenges ...

Advanced multiscale machine learning for nerve conduction velocity analysis.

Scientific reports
This paper presents an advanced machine learning (ML) framework for precise nerve conduction velocity (NCV) analysis, integrating multiscale signal processing with physiologically-constrained deep learning. Our approach addresses three fundamental li...