Using pivot image for the development of composite visual space based on image normalization DOI

Fouzia Idrees,

Awais Adnan

Soft Computing, Год журнала: 2023, Номер unknown

Опубликована: Май 5, 2023

Язык: Английский

Infrared NeRF reconstruction based on perceptual pose and high-frequency-invariant attention DOI
Yuang Chen, Xiaoyu Chen, C. Zhou

и другие.

Signal Processing, Год журнала: 2025, Номер unknown, С. 110012 - 110012

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Large–Small-Scale Structure Blended U-Net for Brightening Low-Light Images DOI Creative Commons
Hao Cheng, Kuo‐Li Pan, Haoxiang Lu

и другие.

Sensors, Год журнала: 2025, Номер 25(11), С. 3382 - 3382

Опубликована: Май 28, 2025

Numerous existing methods demonstrate impressive performance in brightening low-illumination images but fail detail enhancement and color correction. To tackle these challenges, this paper proposes a dual-branch network including three main parts: space transformation, correction (CC-Net), light-boosting (LB-Net). Specifically, we first transfer the input into CIELAB to extract luminosity components. Afterward, employ LB-Net effectively explore multiscale features via carefully designed large–small-scale structure, which can adaptively adjust brightness of images. And use CC-Net, U-shaped network, generate noise-free with vivid color. Additionally, an efficient feature interaction module is introduced for two branches’ information. Extensive experiments on low-light image public benchmarks that our method outperforms state-of-the-art restoring quality Furthermore, further indicate significantly enhances object detection under conditions.

Язык: Английский

Процитировано

0

VCAFusion: A Framework for Infrared and Low Light Visible Image Fusion Based on Visual Characteristics Adjustment DOI Creative Commons
Jiawen Li, Zhengzhong Huang,

Jiapin Peng

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(11), С. 6295 - 6295

Опубликована: Июнь 3, 2025

Infrared (IR) and visible (VIS) image fusion enhances vision tasks by combining complementary data. However, most existing methods assume normal lighting conditions thus perform poorly in low-light environments, where VIS images often lose critical texture details. To address this limitation, we propose VCAFusion, a novel approach for robust infrared scenarios. Our framework incorporates an adaptive brightness adjustment model based on light reflection theory to mitigate illumination-induced degradation nocturnal images. Additionally, design enhancement function inspired human visual perception recover weak further improve quality, develop edge-preserving multi-scale decomposition saliency-preserving strategy, ensuring seamless integration of perceptual features. By effectively balancing fusion, our preserves both the intensity distribution fine details salient objects. Extensive experiments public datasets demonstrate that VCAFusion achieves superior closely aligning with outperforming state-of-the-art qualitative quantitative evaluations.

Язык: Английский

Процитировано

0

MSPIF: Multi-stage progressive visible and infrared image fusion with structures preservation DOI
Biyun Xu, Shaoyi Li, Shaogang Yang

и другие.

Infrared Physics & Technology, Год журнала: 2023, Номер 133, С. 104848 - 104848

Опубликована: Авг. 9, 2023

Язык: Английский

Процитировано

7

Visible and NIR Image Fusion Based on Multiscale Gradient Guided Edge-Smoothing Model and Local Gradient Weight DOI

Dengpeng Zou,

Bin Yang, Yuehua Li

и другие.

IEEE Sensors Journal, Год журнала: 2023, Номер 23(3), С. 2783 - 2793

Опубликована: Янв. 4, 2023

The visible (VS) and near-infrared (NIR) image fusion is a common approach to improve visibility, which saves rich scene details similar colors the VS in fused image. However, preserving edge preventing color distortion fundamental yet challenging problem for VS-NIR work. To address this problem, article proposes novel method based on multiscale gradient guided edge-smoothing (MGES) model local weight. According spectrum characteristics of NIR, weight established by analyzing difference aiming only transfer prominent NIR image, thereby avoiding confusion spectral information. Furthermore, MGES designed simultaneously generate Laplacian pyramid domain filtering-based pyramid, fully considers correlation between neighboring pixels omits Gaussian filtering step, thus effectively spatial suppressing halo artifacts. Subjective objective experimental results demonstrate superiority proposed over state-of-the-art methods terms maintaining naturalness.

Язык: Английский

Процитировано

5

Interactive residual coordinate attention and contrastive learning for infrared and visible image fusion in triple frequency bands DOI Creative Commons
Zhihua Xie,

Sha Zong,

Qiang Li

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 2, 2024

Abstract The auto-encoder (AE) based image fusion models have achieved encouraging performance on infrared and visible fusion. However, the meaningful information loss in encoding stage simple unlearnable strategy are two significant challenges for such models. To address these issues, this paper proposes an model interactive residual attention contrastive learning frequency domain. Firstly, source is transformed into three sub-bands of high-frequency, low-frequency, mid-frequency powerful multiscale representation from prospective spectrum analysis. further cope with limitations straightforward strategy, a learnable coordinate module layer incorporated to adaptively fuse representative characteristics corresponding feature maps. Moreover, leveraged train decomposition network enhancing complementarity at different spectra. Finally, detail-preserving loss, jointly entire good detail maintainability. Qualitative quantitative comparisons demonstrate feasibility validity our model, which can consistently generate images containing both highlight targets legible details, outperforming state-of-the-art methods.

Язык: Английский

Процитировано

1

SFINet: A semantic feature interactive learning network for full-time infrared and visible image fusion DOI
Wenhao Song, Qilei Li, Mingliang Gao

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 261, С. 125472 - 125472

Опубликована: Окт. 5, 2024

Язык: Английский

Процитировано

1

CMRFusion: A cross-domain multi-resolution fusion method for infrared and visible image fusion DOI
Zhang Xiong,

Yuanjia Cao,

Xiaohui Zhang

и другие.

Optics and Lasers in Engineering, Год журнала: 2023, Номер 170, С. 107765 - 107765

Опубликована: Июль 31, 2023

Язык: Английский

Процитировано

3

Contrast enhancement method in aero thermal radiation images based on cyclic multi-scale illumination self-similarity and gradient perception regularization DOI Creative Commons
Yu Wang, Xiubao Sui, Yihong Wang

и другие.

Optics Express, Год журнала: 2023, Номер 32(2), С. 1650 - 1650

Опубликована: Дек. 20, 2023

In aerospace, the effects of thermal radiation severely affect imaging quality infrared (IR) detectors, which blur scene information. Existing methods can effectively remove intensity bias caused by effect, but they have limitations in ability enhancing contrast and correcting local dense or global intensity. To address limitations, we propose a enhancement method based on cyclic multi-scale illumination self-similarity gradient perception regularization solver (CMIS-GPR). First, conceive to correct for amplifying gradient. Specifically, (GPR) directly decomposing degraded image into pair high images, do not contain exhibit inverted directions. However, find that GPR fails area due small scene. Second, cope with cases intensity, regard as sum multiple slight bias. Then, construct (CMIS) model using Gaussian filters structural similarity prior removing layer layer. The result acts coarse correction GPR, does need be overly concerned whether has residuals not. Finally, corrected is input module further residual contrast. Extensive experiments real simulated data demonstrated superiority proposed method.

Язык: Английский

Процитировано

3

Relevance gradient descent for parameter optimization of image enhancement DOI
Yunbo Rao, Yuling Yi, Obed Tettey Nartey

и другие.

Computers & Graphics, Год журнала: 2023, Номер 117, С. 124 - 133

Опубликована: Окт. 27, 2023

Язык: Английский

Процитировано

1