Results 71 to 80 of about 18,768,334 (232)
International Journal for Research in Applied Science and Engineering Technology, 2023 Abstract: The images captured during haze, murkiness and raw weather has serious degradation in them. Image dehazing of a single image is a problematic affair. While already-in-use systems depend on high-quality images, some Computer Vision applications, such self-driving cars and image restoration, typically use input from data that is of poor quality.
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Physical-model guided self-distillation network for single image dehazing
Frontiers in Neurorobotics, 2022 MotivationImage dehazing, as a key prerequisite of high-level computer vision tasks, has gained extensive attention in recent years. Traditional model-based methods acquire dehazed images via the atmospheric scattering model, which dehazed favorably but ...
Yunwei Lan +5 more
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Electronics Letters, Volume 62, Issue 1, January/December 2026.PhysMamba‑Unroll, a framework that addresses the high computational cost of deploying deep unfolding networks on edge devices by synergistically integrating Mamba's linear complexity with physics‐informed unrolling. ABSTRACT Real‐time adverse weather image restoration on edge devices is hindered by a fundamental tension: Deep unfolding networks (DUNs ...
Yongkang Li +4 more
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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 +3 more
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Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning
Information, 2017 Images captured in bad conditions often suffer from low contrast. In this paper, we proposed a simple, but efficient linear restoration model to enhance the low contrast images.
Gengfei Li, Guiju Li, Guangliang Han
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Progressive Knowledge Distillation for Edge‐Deployable Solder Joint Segmentation
IET Image Processing, Volume 20, Issue 1, January/December 2026.Solder‐Yolo is a lightweight deep learning model based on YOLOv8‐seg, designed for high‐precision solder joint inspection in FPC ribbon cables. It incorporates model pruning, knowledge distillation and a hierarchical context attention module to achieve 96.7% precision and 91.3% mAP while maintaining high inference speed.
Kunhong Li +4 more
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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 +4 more
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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 +4 more
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SINGLE IMAGE DEHAZING USING PHYSICS-INFORMED CONVOLUTIONAL AUTOENCODER
Модели, системы, сети в экономике, технике, природе и обществеBackground. Generally, haze can be considered to be one of the most fundamental phenomena causing image visibility degradation. Numerous haze removal approaches have been proposed and most of them have achieved significant progress.
A.V. Kozhevnikova, M.A. Mitrokhin
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GRA‐Net: Geometry‐ and Response‐Aware Convolutional Network for Nighttime Deflaring
IET Image Processing, Volume 20, Issue 1, January/December 2026.We propose GRA‐Net, a geometry‐ and response‐aware convolutional network for nighttime flare removal in autonomous driving images. The network achieves unified spatio‐photometric flare rectification by leveraging geometric priors and adaptive illumination responses. Experiments on the Flare7K++ dataset show that GRA‐Net outperforms existing lightweight
Wei Lu +5 more
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