Results 61 to 70 of about 6,596,130 (203)
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 +4 more
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Low Light Image Adaptive Enhancement Algorithm Based on Retinex Theory [PDF]
Jisuanji kexueImages in real-world environments are often shot under sub-optimal lighting conditions,resulting in insufficient brightness and poor visual experience.Existing low-light image enhancement methods are often complex in structure and focus on improving the ...
ZHENG Dichen, HE Jikai, LIU Yi, GAO Fan, ZHANG Dengyin
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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 +3 more
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An Image Dehazing Method Based On an Improved Retinex Theory [PDF]
Proceedings of the 2016 International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2016), 2016 Image quality is often affected in many ways by the atmosphere, especially in foggy weather conditions. Dehazing is a highly demanded operation within the domain of image processing for various applications. The paper proposes a new single-image dehazing algorithm based on the single scale retinex (SSR) algorithm, combined with the theory of ...
Hong Wang +3 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
wiley +1 more source
New Method of Image Background Suppression Based on Soft Morphology and Retinex Theory
Journal of Electrical and Computer Engineering, 2015 A new river flow measurement method based on graphic process has been proposed recently, which gets the velocity in optical imaging modality through measuring the continuous displacement of floating debris, then reconstructs a two-dimensional river ...
Lili Zhang +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
wiley +1 more source
Low‐light image enhancement based on exponential Retinex variational model
IET Image Processing, 2021 Aiming at the problems of residual noise, low contrast, and limited detail information caused by low‐light images, this paper proposes a new Retinex variational model.
Xinyu Chen, Jinjiang Li, Zhen Hua
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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 +4 more
wiley +1 more source
Development of Optimized Adaptive Multiscale Retinex Deep Learning Model for Image Enhancement
IEEE AccessHigh-quality image plays a crucial role in many applications, including medical diagnosis, communications and remote sensing and reflect the details of the target scene more clearly, which guarantee the subsequent image processing strongly.
Lakshmi Kumari, Neetu Mittal, Megha Modi
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