Color fusion method for low-light-level and infrared images in night vision

Yang, Shaokui; Wen, Liu; Deng, Chan; Zhang, Xin

doi:10.1109/cisp.2012.6469757

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…Yu et al 26 used SR for medical image fusion with K-singular value decomposition (K-SVD) algorithm, 27 whereas Li and Yang 28 first performed pan-sharpening (a form of image fusion) based on SR. Yin et al 29 conducted simultaneous image fusion and super-resolution using SR for medical and remote sensing images. Yang et al 30 proposed a color fusion scheme for infrared and lowlight-level images with SR used for the fused image of a particular channel. Liu et al, 31 proposed a technique for simultaneous fusion and denoising called adaptive SR. Liu et al 32 conducted fusion of medical images through NSCT and SR in which Laplacian pyramid (LP) and high-pass (HP) coefficients were calculated by NSCT for source images followed by LP bands fused with SR.…”

Section: Literature Reviewmentioning

confidence: 99%

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Medical image fusion by sparse-based modified fusion framework using block total least-square update dictionary learning algorithm

Saini

Mathur

2022

J. Med. Imag.

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Purpose:The technique of fusing or integrating medical images collected from single and various modalities is known as medical image fusion. This is done to improve the quality of the images and combine information from several medical images. The whole procedure aids medical practitioners in gaining correct information from single images. Image fusion is one of the fastest-growing research topics in the medical imaging field. Sparse modeling is a popular signal representation technique used for image fusion with different dictionary learning approaches. We propose a medical image fusion with sparse representation (SR) and block total least-square (BLOTLESS) update dictionary learning.Approach: The domain of dictionary learning is the most significant research domain related to SR. An efficient dictionary increases the effectiveness of sparse modeling. Due to SR being an ongoing interesting research area, the medical image fusion process is done with a modified image fusion framework with recently developed BLOTLESS update dictionary learning. Results:The experimental results are compared for the image fusion process using other stateof-the-art dictionary learning algorithms, such as simultaneous codeword optimization, method of optimal directions, and K-singular value decomposition. The effectiveness of the algorithm is evaluated based on image fusion quantitative parameters. Results show that the BLOTLESS update dictionary algorithm is a promising modification for the sparse-based image fusion with its applicability in the fusion of images related to different diseases. Conclusions:The experiments and results show that the dictionary learning algorithm plays an important role in the sparse-based image fusion general framework. The fusion results also show that the proposed improved image fusion framework for medical images is promising compared with frameworks with other dictionary learning algorithms. As an application, it is also used as a tool for the fusion of different modularities of images related to brain tumor and glioma.

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Section: Literature Reviewmentioning

confidence: 99%

“…conducted simultaneous image fusion and super-resolution using SR for medical and remote sensing images. Yang et al 30 . proposed a color fusion scheme for infrared and low-light-level images with SR used for the fused image of a particular channel.…”

Section: Literature Reviewmentioning

confidence: 99%

Medical image fusion by sparse-based modified fusion framework using block total least-square update dictionary learning algorithm

Saini

Mathur

2022

J. Med. Imag.

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“…To illustrate, we can cite the frequent case of color and infrared image fusion from day and night vision cameras, frequently used in surveillance and security context [1]- [3]. Another example is the fusion of low-light and infrared images enabling color night vision system [4]. Also, the ADAS 1 and UAV 2 systems benefit from such multi-modal approaches increasing the capabilities of such systems [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the number of 'active sensors' used at the moment can dynamically change with respect to the luminosity conditions or the applicative requirements. This makes the design of computing resources very arduous task, 1 Advanced driver assistance systems 2 Unmanned Aerial Vehicles The complexity of the computationally efficient hardware design for multi-sensor systems is illustrated by the numerous publications dealing with some individual key problems of this challenge. Thus in [8] the authors develop and implement an FPGA-based scalable and resource-efficient multi-camera IP core for image reconstruction, however, the performance decreases with the number of sensors.…”

Section: Introductionmentioning

confidence: 99%

Highly Scalable Monitoring System on Chip for Multi-Stream Auto-Adaptable Vision System

Isavudeen

Ngan

Dokladalova

et al. 2017

Proceedings of the International Conference on Research in Adaptive and Convergent Systems

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International audienceThe integration of multiple and technologically heterogeneous sensors (infrared, color, etc) in vision systems tend to democratize. The objective is to benefit from the multi-modal perception allowing to improve the quality and ro-bustness of challenging applications such as the advanced driver assistance, 3-D vision, inspection systems or military observation equipment. However, the multiplication of heterogeneous processing pipelines makes the design of efficient computing resources for the multi-sensor systems very arduous task. In addition to the context of latency critical application and limited power budget, the designer has often to consider the parameters of sensors varying dynamically as well as the number of active sensors used at the moment. To optimize the computing resource management, we inspire from the self-aware architectures. We propose an original on-chip monitor, completed by an observation and command network-on-chip allowing the system resources supervision and their on-the-fly adaptation. We present the evaluation of the proposed monitoring solution through FPGA implementation. We estimate the cost of the proposed solution in the terms of surface occupation and latency. And finally, we show that the proposed solution guarantees a processing of 1080p resolution frames at more than 60 fps

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“…Most of these methods can be divided into two categories which are spatial domain fusion and transform domain fusion. Fusion methods in spatial domain are directly performed in pixel gray level 7 or color space, 8 such as linear weight method, principal component analysis (PCA) method, and pseudo color image fusion method. For methods in transform domain, source images are converted into a transformed representation, then different fusion rules are applied to different scales and resolutions, and finally the inverse transform is used to obtain the fused image.…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor image fusion based on regional characteristics

Meng

Shi

Shan

et al. 2017

International Journal of Distributed Sensor Networks

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Multi-sensor data fusion method has been widely investigated in recent years. This article presents a novel fusion algorithm based on regional characteristics for combining infrared and visible light images in order to achieve an image with clear objects and high-resolution scene. First, infrared objects are extracted by region growing and guided filter. Second, the whole scene is divided into the objects region, the smooth region, and the texture region according to different regional characteristics. Third, the non-subsampled contourlet transform is used on infrared and visible images. Then, different fusion rules are applied to different regions, respectively. Finally, the fused image is constructed by the inverse non-subsampled contourlet transform with all coefficients. Experimental results demonstrate that the proposed objects extraction algorithm and the fusion algorithm have good performance in objective and subjective assessments.

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Color fusion method for low-light-level and infrared images in night vision

Cited by 5 publications

References 7 publications

Medical image fusion by sparse-based modified fusion framework using block total least-square update dictionary learning algorithm

Medical image fusion by sparse-based modified fusion framework using block total least-square update dictionary learning algorithm

Highly Scalable Monitoring System on Chip for Multi-Stream Auto-Adaptable Vision System

Multi-sensor image fusion based on regional characteristics

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