PCNN double step firing mode for image edge detection

Deng, Xiangyu; Yang, Yahan; Zhang, Huan; Ma, Yide

doi:10.1007/s11042-022-12725-2

Cited by 5 publications

(1 citation statement)

References 47 publications

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“…A thermography is used as the input source which can trigger the spiking response by the array of TaO X -based AST pixelby-pixel, and the thermography is then imaged to a map of spiking rates. Finally, the spiking rate of map wad processed by a PCNN [28], [29] for edge detection. The PCNN consists of a single layer two-dimensional neural network that can perform intelligent tasks even without training.…”

Section: Resultsmentioning

confidence: 99%

An Oxide Based Spiking Thermoreceptor for Low-Power Thermography Edge Detection

Shi

Heng

Wang

et al. 2022

IEEE Electron Device Lett.

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Thermoreceptors can encode thermal stimuli into spikes that are processed by the neural network to endow human with thermal perception. Such energy-efficiency and robust interaction with real-world have inspired the rise of the artificial spiking thermoreceptor (AST). However, monolithic spiking thermoreceptor is still rarely reported, which may due to the lack of device that can simultaneously implement temperature-sensing and spikeencoding functions. Here, we demonstrate an artificial spiking thermoreceptor based on Ag/TaO X /AlO X /ITO threshold switching memristor to achieve human-like thermal perception. The device is able to encode thermal information into spikes at a low power consumption (<240 nW). These advantages consequently facilitate the demonstration of power efficient and accurate thermography edge detection based on the array of such AST and a pulse coupled neural network (PCNN).

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Section: Resultsmentioning

confidence: 99%

An Oxide Based Spiking Thermoreceptor for Low-Power Thermography Edge Detection

Shi

Heng

Wang

et al. 2022

IEEE Electron Device Lett.

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Image denoising and segmentation model construction based on IWOA-PCNN

Zhang

2023

Sci Rep

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The research suggests a method to improve the present pulse coupled neural network (PCNN), which has a complex structure and unsatisfactory performance in image denoising and image segmentation. Then, a multi strategy collaborative improvement whale optimization algorithm (WOA) is proposed, and an improved whale optimization algorithm (IWOA) is constructed. IWOA is used to find the optimal parameter values of PCNN to optimize PCNN. By combining the aforementioned components, the IWOA-PCNN model had the best image denoising performance, and the produced images were crisper and preserve more information. IWOA-PCNN processed pictures have an average PSNR of 35.87 and an average MSE of 0.24. The average processing time for photos with noise is typically 24.80 s, which is 7.30 s and 7.76 s faster than the WTGAN and IGA-NLM models, respectively. Additionally, the average NU value measures 0.947, and the average D value exceeds 1000. The aforementioned findings demonstrate that the suggested method can successfully enhance the PCNN, improving its capability for image denoising and image segmentation. This can, in part, encourage the use and advancement of the PCNN.

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A Novel Defocus-Blur Region Detection Approach Based on DCT Feature and PCNN Structure

Basar,

Ali,

Waheed

et al. 2023

IEEE Access

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The motion or out-of-focus effect in digital images is the main reason for the blurred regions in defocused-blurred images. It may adversely affect various image features such as texture, pixel, and region. Therefore, it is important to detect in-focused objects in defocused-blurred images after the segmentation of blurred and non-blurred regions. The state-of-the-art techniques are prone to noisy pixels, and their local descriptors for developing segmentation metrics are also complex. To address these issues, this research, therefore, proposed a novel and hybrid focused detection approach based on Discrete Cosine Transform (DCT) coefficients and PC Neural Net (PCNN) structure. The proposed approach partially resolves the limitations of the existing contrast schemes to detect focused smooth objects from the outof-focused smooth regions in the defocus dataset. The visual and quantitative evaluation illustrates that the proposed approach outperformed in terms of accuracy and efficiency to referenced algorithms. The highest ℱ 𝛼 -score of the proposed approach on Zhao's dataset is 0.7940 whereas on Shi's dataset is 0.9178.

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PCNN double step firing mode for image edge detection

Cited by 5 publications

References 47 publications

An Oxide Based Spiking Thermoreceptor for Low-Power Thermography Edge Detection

An Oxide Based Spiking Thermoreceptor for Low-Power Thermography Edge Detection

Image denoising and segmentation model construction based on IWOA-PCNN

A Novel Defocus-Blur Region Detection Approach Based on DCT Feature and PCNN Structure

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