Electrical capacitance tomography (ECT) image reconstruction has developed decades and made great achievements, but there is still a need to find new theory framework to make image reconstruction ...

Thanks to the remarkable advances in generative adversarial networks (GANs), it is becoming increasingly easy to generate/manipulate images. The existing works have mainly focused on deepfake in face ...

Weather image classification is a critical component of the vision systems in autonomous driving systems (ADSs), facilitating accurate decision-making across diverse driving conditions. Adverse ...

However, traditional CNNs require fixed-size inputs, which are a limitation when dealing with HRSI that represent large image domains, potentially degrading classification performance. To overcome ...

In recent years, convolutional neural networks (CNNs) have been impressive due to their excellent feature representation abilities, but it is difficult to learn long-distance spatial structures ...

This article introduces a two-phase learning approach for hyperspectral image (HSI) classification using few-shot learning (FSL). For the first phase, we present a novel spatiospectral masked ...

This research introduces an advanced approach to automate the segmentation and quantification of nuclei in fluorescent images through deep learning techniques. Overcoming inherent challenges such as ...

Furthermore, an experienced radiologist blindly rated the 250 dental US images on a scale of 1 to 5, with 5 being the highest image quality, showing that HCTSpeckle consistently produced ...

Reversible image conversion (RIC) aims to build a reversible transformation between specific visual content (e.g., short videos) and an embedding image, where the original content can be restored from ...

Image compression is a fundamental technique in digital image processing used to decrease the space used for storage of digital images and videos, which will help to increase the storage space and for ...

Multi-stage strategies are frequently employed in image restoration tasks. While transformer-based methods have exhibited high efficiency in single-image super-resolution tasks, they have not yet ...

Recent end-to-end image compressive sensing networks primarily use Convolutional Neural Networks (CNNs) and Transformers, each with distinct limitations: CNNs struggle with global feature capture, ...