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Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN‐ViT Model
Food Science &Nutrition, Volume 13, Issue 7, July 2025.A hybrid CNN‐ViT model was proposed to improve maize leaf disease classification by leveraging both local and global image features. The model achieved 99.15% accuracy, with precision, recall, and F1‐score all at 99.13%, outperforming standalone CNN and ViT architectures.
Gunjan Shandilya +5 more
wiley +1 more source
Interdisciplinary Medicine, Volume 3, Issue 4, July 2025.This review explores the applications of Generative AI (GAI) in medical imaging, with emphasis on its potential to enhance AI training and personalized medicine. The study comprehensively examines frameworks for evaluating the validity of GAI‐generated images while identifying critical challenges including model bias, data augmentation reliability, and
Wenle He +6 more
wiley +1 more source
DUNet: a novel dehazing model based on outdoor images
Frontiers in Plant ScienceImage dehazing technology is widely utilized in outdoor environments, especially in precision agriculture, where it enhances image quality and monitoring accuracy.
Wei Zhao +10 more
doaj +1 more source
Image dehazing by artificial multiple-exposure image fusion [PDF]
Signal Processing, 2018 Bad weather conditions can reduce visibility on images acquired outdoors, decreasing their visual quality. The image processing task concerned with the mitigation of this effect is known as image dehazing. In this paper we present a new image dehazing technique that can remove the visual degradation due to haze without relying on the inversion of a ...
openaire +1 more source
End-to-End United Video Dehazing and Detection
, 2017 The recent development of CNN-based image dehazing has revealed the effectiveness of end-to-end modeling. However, extending the idea to end-to-end video dehazing has not been explored yet.
Feng, Dan +4 more
core +1 more source
Proximal Alternating Direction Network: A Globally Converged Deep Unrolling Framework
, 2017 Deep learning models have gained great success in many real-world applications. However, most existing networks are typically designed in heuristic manners, thus lack of rigorous mathematical principles and derivations.
Cheng, Shichao +4 more
core +1 more source
Fast No-reference Deep Image Dehazing
Machine Vision and ApplicationsAbstract This paper presents a deep learning method for image dehazing and clarification.The main advantages of the method are high computational speed and usingupaired image data for training. The method adapts the Zero-DCE approach for the image dehazing problem and uses high-order curves to adjust the dynamicrange of images and achieve ...
Hongyi Qin, Alexander G. Belyaev
openaire +1 more source
An image dehazing method combining adaptive dual transmissions and scene depth variation
Journal of Measurement Science and Instrumentation, 2023 Aiming at the problems of imprecise transmission estimation and color cast in single image dehazing algorithms, an image dehazing method combining adaptive dual transmissions and scene depth variation is proposed.
LIN Lei, YANG Yan
doaj
Enhance Dehazed Images Rapidly Without Losing Restoration Accuracy
IEEE AccessWe proposed a novel image-enhancing framework to ensure consolidated restoration accuracy when remedying the visual quality of dehazed images, such as over-saturation, color deviation, or luminance issues. Conventionally, the dehazing process was usually
Ping-Juei Liu
doaj +1 more source
Multi-level fusion dehazing network based on learning of hazy layers
Journal of Measurement Science and Instrumentation, 2023 Aiming at the problems such as color cast and incomplete haze removal in dehazing algorithms, a multi-level feature fusion network based on the learning of hazy layers is proposed for single image dehazing.
WANG Rong, YANG Yan
doaj