Weighted guided image filtering for image enhancement

Karumuri, Rajasekhar; Kumari, Sangu Aruna

doi:10.1109/cesys.2017.8321137

Cited by 12 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Anomaly targets, especially subpixel targets showing sparsity against the background, we exploit the guided filter to smooth the image and achieve a robust background description. The guided filter has shown satisfactory ability to preserve the large scale of edges and smooth background information [24,25] respectively. The guided filter is driven by a local linear model as follows:…”

Section: ) Original Guided Filtermentioning

confidence: 99%

A Novel Filter-Based Anomaly Detection Framework for Hyperspectral Imagery

Wang¹,

Song

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Hyperspectral images stand out from other remote sensing images in anomaly target detection because they contain unique distinguishing spectral information and attract great interest in applications of search and rescue. However, most of the popular techniques for hyperspectral anomaly detection tasks focus on improving accuracy with complicated algorithms and face difficulty in efficiently balancing performance and complexity. In this paper, we propose a novel anomaly detection approach using a selected band image extracted from the band selection model combined with an image filter. Singular value decomposition (SVD) is adopted for spectral dimensionality reduction. A dual-window guided filter is constructed to highlight the potential anomaly targets. To quickly calculate the abnormity degree, we design an efficient diagonal matrix operation to achieve the energy of each pixel, and a spatial regulation model is designed to enhance the subpixel target detection performance. Extensive experiments conducted on two real-world hyperspectral datasets demonstrate that, compared with the existing relevant state-of-the-art approaches, the proposed method requires less detection time and achieves higher detection accuracy.

show abstract

Section: ) Original Guided Filtermentioning

confidence: 99%

A Novel Filter-Based Anomaly Detection Framework for Hyperspectral Imagery

Wang¹,

Song

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Specifically, the neurons in the central region are excited to the central neuron for ON-CVRF while they are inhibitory for OFF-CVRF; conversely, the neurons in the surrounding region for ON-CVRF and OFF-CVRF are inhibitory and excited to the central neuron, respectively. Thus, Rodieck and Stone (Karumuri and Kumari 2017), in 1965, attempted to model above phenomenons in CVRF by using the difference of two Gaussians (DOG) followed by…”

Section: Cvrf and Its Parameters Estimationmentioning

confidence: 99%

“…However, these techniques do not take the imprecision of gray values into account. Thereby some filtering-based methods (Yang et al 2003;Karumuri and Kumari 2017;Bhadu et al 2017) and neural network-based methods (Tao et al 2017;Park et al 2018;Ma et al 2007;Zhang et al 2010;Xu et al 2014) are developed.…”

Section: Introductionmentioning

confidence: 99%

Pulse coupled neural network based MRI image enhancement using classical visual receptive field for smarter mobile healthcare

Nie

Cao

et al. 2018

J Ambient Intell Human Comput

View full text Add to dashboard Cite

With the rapid growth of medical big data, medical signal processing measurement techniques are facing severe challenges. Enormous medical images are constantly generated by various health monitoring and sensing devices, such as ultrasound, MRI machines. Hence, based on pulse coupled neural network (PCNN) and the classical visual receptive field (CVRF) with the difference of two Gaussians (DOG), a contrast enhancement of MRI image is suggested to improve the accuracy of clinical diagnosis for smarter mobile healthcare. As one premise, the parameters of DOG are estimated from the fundamentals of CVRF; then the PCNN parameters in image enhancement are estimated eventually with the help of DOG. As a result, the MRI images can be enhanced adaptively. Due to the exponential decay of the dynamic threshold and the pulses coupling among neurons, PCNN effectively enhances the contrast of low grey levels in MRI image. Moreover, because of the inhibitory effects from inhibitory region in CVRF, PCNN also effectively preserves the structures such as edges for enhanced results. Experiments on several MRI images show that the proposed method performs better than other methods by improving contrast and preserving structures well.

show abstract

Structure revealing of low-light images using wavelet transform based on fractional-order denoising and multiscale decomposition

Rahman

Aamir

et al. 2020

Vis Comput

View full text Add to dashboard Cite

Weighted guided image filtering for image enhancement

Cited by 12 publications

References 15 publications

A Novel Filter-Based Anomaly Detection Framework for Hyperspectral Imagery

A Novel Filter-Based Anomaly Detection Framework for Hyperspectral Imagery

Pulse coupled neural network based MRI image enhancement using classical visual receptive field for smarter mobile healthcare

Structure revealing of low-light images using wavelet transform based on fractional-order denoising and multiscale decomposition

Contact Info

Product

Resources

About