Results 81 to 90 of about 2,348 (159)
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
Color Consistency and Local Contrast Enhancement for a Mobile Image-Based Change Detection System
Mobile change detection systems allow for acquiring image sequences on a route of interest at different time points and display changes on a monitor.
Marco Tektonidis, David Monnin
doaj +1 more source
Pre‐Trained Codebook‐Based Enhancement: A Novel Approach for Clarifying Underwater Images
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
Retinex theory for color image enhancement: A systematic review [PDF]
A short but comprehensive review of Retinex has been presented in this paper. Retinex theory aims to explain human color perception. In addition, its derivation on modifying the reflectance components has introduced effective approaches for images ...
Sabri, Rooa Adnan +5 more
core +1 more source
YOLO‐O: An Improved YOLO‐Based Framework for Vehicle Detection
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 +4 more
wiley +1 more source
Mean‐Local Binary Pattern‐Guided Multi‐Attention Network for Low‐Light Image Enhancement
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
LCH‐Net: A Lightweight OKLCH‐Space Decoupling Network for Archival Image Enhancement
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
In view of the shortcomings of the total variational Retinex model which use the total variation (TV) of the reflection as the regularization.An extension of TV regularization with the concept of relative gradient was introduced and finally a new ...
Ning ZHI, Shan-jun MAO, Mei LI
doaj +2 more sources
Effective color correction method employing HSV color model
This paper suggests a new algorithm to solve problems of the current retinex algorithm such as distortion of grey out and color noise due to the individual treatment of RGB channel and log function,and halo effect occurred by use of the Gaussian filter ...
Jae-hyoung YU1 +4 more
doaj
RailNet: Railway Track Anomaly Detection via Image Processing With Hybrid Deep Learning Techniques
Our model leverages a DenseNet121 backbone combined with advanced neural network layers to achieve high accuracy in identifying and classifying track defects. The dataset, consisting of images of railway tracks with and without faults, was rigorously augmented to enhance model robustness.
Umair Saeed +8 more
wiley +1 more source

