Sparse representation and homomorphic filter for capsule endoscopy image enhancement

Bhadu, Rampal; Sharma, Rahul; Soni, Surender Kumar; Varma, Nithin

doi:10.1109/ccaa.2017.8229976

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…(15) = ln 255 254 = 0.0039. equalization (CLAHE) (Sasi and Jayasree 2013), homomorphic filtering (HF) (Bhadu et al 2017), and probabilistic method with simultaneous illumination and reflectance estimation (PMSIRE) (Fu et al 2015). Moreover, four quantitative criterions are selected in the objective evaluation for the enhanced images, which include entropy (EN) (Li and Xie 2015), contrast improvement index (CII) (Yang and Zhang 2012), structural similarity (SSIM) (Wang et al 2004) and edge preserve index (EPI) (Zhang et al 2011).…”

Section: Experiments Results and Analysismentioning

confidence: 99%

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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

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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.

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Section: Experiments Results and Analysismentioning

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

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“…Retinex theory states that colour texture and light are constant feature and variable feature, respectively. The theory suggests that adjusting variable features to the retina can distinguish constant features and minimize the loss of constant features [7]. Homomorphic filtering establishes a reflection-illumination model in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Colour Variation Minimization Retinex Decomposition and Enhancement with a Multi-Branch Decomposition Network

Deng

Pang

2023

Chinese J. Elect.

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This paper proposes a colour variation minimization retinex decomposition and enhancement with a multi-branch decomposition network (CvmD-net) to remove single image darkness. The network overcomes the problem that retinex deep learning model relies on matching bright images to process dark images. Specifically, our method takes two stages to light up the darkness in initial images: image decomposition and brightness optimization. We propose an input constant feature prior mechanism (ICFP) based on reflection constant features. The mechanism extracts structure and colour from the input images and constrains the reflected images output from the decomposition model to reduce color distortion and artifacts. The noise amplification during decomposition is addressed by a multi-branch decomposition network. Sub-networks with different structures are employed to focus on different prediction tasks. This paper proposes a reference mechanism for input brightness. This mechanism optimizes the output brightness distribution by calculating the reference brightness of the dark images. Experimental results on two benchmark datasets, namely, LOL and ZeroDCE, demonstrate that the proposed method can better balance dense noise interference and colour restoration. For the evaluation on real images, we collect Skynet images at night to verify the performance of the proposed approach. Compared with the state-of-the-art non-reference retinex decomposition-enhancement models, this paper has the best brightness optimization.

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Improvement of image quality of digital holographic reconstruction and development of related systems

Ren

Niu

2022

Multimed Tools Appl

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Sparse representation and homomorphic filter for capsule endoscopy image enhancement

Cited by 3 publications

References 13 publications

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

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

Colour Variation Minimization Retinex Decomposition and Enhancement with a Multi-Branch Decomposition Network

Improvement of image quality of digital holographic reconstruction and development of related systems

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