Results 71 to 80 of about 11,243,828 (208)
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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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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Region Adaptive Single Image Dehazing [PDF]
Entropy, 2021 Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility. This problem has been extensively studied in the literature, and the most popular technique is dark channel prior (DCP). However, dark channel prior tends to underestimate
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Image dehazing using two‐dimensional canonical correlation analysis
IET Computer Vision, 2015 Image dehazing is an important issue that interests both image processing and computer vision. In this study, image dehazing is modelled as an example‐based learning problem, and a novel dehazing algorithm using two‐dimensional (2D) canonical correlation
Liqian Wang, Liang Xiao, Zhihui Wei
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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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BidNet : Binocular Image Dehazing without explicit disparity estimation [PDF]
, 2020 Heavy haze results in severe image degradation and thus hampers the performance of visual perception, object detection, etc. On the assumption that dehazed binocular images are superior to the hazy ones for stereo vision tasks such as 3D object detection
Nie, Jing +9 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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Reliable image dehazing by NeRF
Optics ExpressImage dehazing is a typical low-level visual task. With the continuous improvement of network performance and the introduction of various prior knowledge, the ability of image dehazing is becoming stronger. However, the existing dehazing methods have problems such as the inability to obtain real shooting datasets, unreliable dehazing processes, and the
Zheyan Jin +4 more
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