Results 31 to 40 of about 919 (135)

Reviewing seas of data: Integrating image‐based bio‐logging and artificial intelligence to enhance marine conservation

Methods in Ecology and Evolution, Volume 17, Issue 2, Page 272-290, February 2026.
Abstract Conservation of marine ecosystems can be improved through a better understanding of ecosystem functioning, particularly the cryptic underwater behaviours and interactions of marine predators. Image‐based bio‐logging devices (including images, videos and active acoustic) are increasingly used to monitor wildlife movements, foraging behaviours ...
Marianna Chimienti, Akiko Kato, Vahid Seydi, Stefan Schoombie, Jefferson T. Hinke, Ruth Joy, Damian C. Lidgard, W. Chris Oosthuizen, Yannis P. Papastamatiou, Yan Ropert‐Coudert, Akinori Takahashi, Jean‐Baptiste Thiebot, Muriel Visani, Mickael Coustaty, Tiphaine Jeanniard‐du‐Dot   +14 more
wiley   +1 more source

Properly Speaking, Sir Isaac Newton Was the First Perceptual Psychologist and, Therefore, the First Psychologist

Color Research &Application, Volume 51, Issue 1, January/February 2026.
Newton’s insight that “the rays are not colored” anticipated a constructivist view of perception in which physical stimuli provide input but perceptual qualities arise from neural processing. Framed by Newton’s view, early sensory psychology and modern neuroscience converged on the conclusion that color is an internally generated percept, shaped by ...
Billy R. Wooten, Billy R. Hammond
wiley   +1 more source

Research on Tunnel Pedestrian Detection Algorithm Based on Image Enhancement and Threshold Segmentation

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This paper proposes a low‐light image enhancement and denoising algorithm tailored for tunnel scenes based on computer vision and deep learning technologies. On this basis, a tunnel pedestrian detection method based on connected domain dynamic threshold segmentation is designed, which can reduce the computational resources for identifying pedestrian ...
Yudan Tian, Tong Zhou, Dongchun Li, YuZhao Huang, Hua Huang   +4 more
wiley   +1 more source

Wavelet Quadtree Contrast Limited Adaptive Histogram Equalisation Tablet Enhancement Method for Defect Detection in Low‐Contrast Tablets

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This paper proposes a novel image enhancement method, WCTE, which integrates Haar wavelet transform and adaptive CLAHE to improve the visibility of low‐contrast tablet images. Combined with the YOLOv11 model, this approach significantly boosts defect detection accuracy, especially for half‐grain and paste tabtal.
Zimei Tu, Qinna Wang, Zhicheng Jiang, Jinhua Jiang   +3 more
wiley   +1 more source

Dual‐Path Wavelet Transform Image Exposure Correction Algorithm

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This paper proposes an image exposure correction method, combining wavelet transforms and deep learning. It uses a dual‐path approach: luminance‐related low‐frequency components are corrected by an exposure correction network, while texture‐detail high‐frequency components are enhanced by a residual network.
Kaicheng Xu, Xingchen Duan, Wei Zhao, Keqin Gan   +3 more
wiley   +1 more source

An Image Dehazing Algorithm for Underground Coal Mine Environments Based on Dual‐Channel Prior and Adaptive Contrast Enhancement

IET Image Processing, Volume 20, Issue 1, January/December 2026.
In this paper, we introduce a novel image dehazing algorithm based on dual‐channel prior adaptive contrast‐limited enhancement. The algorithm estimates model parameters from different perspectives based on dual‐channel prior knowledge and fuses the parameters according to the characteristics of each channel.
Chang Su, Dong Bing Xiang, Liang Liang Zhao, Jia Qing Meng, Jian Lu   +4 more
wiley   +1 more source

Pre‐Trained Codebook‐Based Enhancement: A Novel Approach for Clarifying Underwater Images

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This work presents a codebook‐driven enhancement network to tackle colour distortion and detail loss in underwater images. By aligning multi‐scale features with a pre‐trained VQGAN codebook and fusing shallow‐to‐deep cues, the method boosts contrast, edges and clarity without requiring large paired datasets.
Yuanxue Xin, Zhi Qiao, Xinnan Fan, Chen Guo, Bing Zhao   +4 more
wiley   +1 more source

YOLO‐O: An Improved YOLO‐Based Framework for Vehicle Detection

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This study proposes a YOLO‐O model, based on YOLOv7, for improved detection of tiny vehicles on roads. Enhancements like residual networks, Squeeze and Excitation Network (SENet), coordinate attention, and SIoU regression loss boost precision, especially for small objects.
Rabbia Mahum, Usama Irshad, Ahmed M. El‐Sherbeeny, Mohammad Shehab, Seongkwan Mark Lee   +4 more
wiley   +1 more source

Mean‐Local Binary Pattern‐Guided Multi‐Attention Network for Low‐Light Image Enhancement

IET Image Processing, Volume 20, Issue 1, January/December 2026.
Low‐light image enhancement struggles with noise amplification, residual dark areas, artefacts and detail loss. This paper presents the MGA‐LLIEN network, which uses M‐LBP for adaptive brightness adjustment and detail recovery while reducing noise and outperforms leading methods in tests. ABSTRACT Low‐light image enhancement faces key challenges: noise
Binxin Tang, Xianchun Zhou, Jiewen Tian, Mengnan Lv, Ying Zhang   +4 more
wiley   +1 more source

LCH‐Net: A Lightweight OKLCH‐Space Decoupling Network for Archival Image Enhancement

IET Image Processing, Volume 20, Issue 1, January/December 2026.
This study puts forward the LCH‐Net framework. This method processes lightness, chroma and hue separately in the OKLCH colour space: the lightness adjustment sub‐network adaptively regulates illumination via a learnable lightness curve; the chroma adjustment sub‐network combines frequency‐domain and spatial‐domain denoising to suppress noise while ...
Liyang Yu, Ruilin Deng, Huaying Liu
wiley   +1 more source