2022
DOI: 10.1111/coin.12510
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Addressing image and Poisson noise deconvolution problem using deep learning approaches

Abstract: Digital images are more important in numerous contemporary applications, and the need for images in the technical field is also increasing drastically. It is used to recognize signatures and faces in many industries and is applicable for intelligent departments. The images are usually associated with the noise content; this may happen due to the instrument imperfections, troubleshooting while collecting data from the acquisition process, and another natural phenomenon. Poisson noise, also known as photon noise… Show more

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Cited by 23 publications
(3 citation statements)
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“…Conventional deconvolution image reconstruction techniques fundamentally hinge on approximation and iterative optimization processes. Regrettably, these modalities are highly susceptible to various disturbances and instabilities, rendering them inadequately robust and incapable of achieving high-resolution imaging at low signal-to-noise ratios (SNR). , Additionally, the symbiotic interplay between the image information frequency shift, engendered by encoding structures on the scintillator, and noise signifies that image encoding predicated on nanophotonic scintillators is not a purely convolutional process, thereby complicating the attainment of precise deconvolution resolutions. Of late, deep learning (DL) has emerged as a formidable technique for resolving highly ill-posed computational imaging quandaries. In a pioneering investigation, Sinha and his cadre of researchers were the pioneers in successfully training deep neural networks (DNN) to address inverse problems in computational imaging, illuminating its potential in lensless imaging .…”
Section: Introductionmentioning
confidence: 99%
“…Conventional deconvolution image reconstruction techniques fundamentally hinge on approximation and iterative optimization processes. Regrettably, these modalities are highly susceptible to various disturbances and instabilities, rendering them inadequately robust and incapable of achieving high-resolution imaging at low signal-to-noise ratios (SNR). , Additionally, the symbiotic interplay between the image information frequency shift, engendered by encoding structures on the scintillator, and noise signifies that image encoding predicated on nanophotonic scintillators is not a purely convolutional process, thereby complicating the attainment of precise deconvolution resolutions. Of late, deep learning (DL) has emerged as a formidable technique for resolving highly ill-posed computational imaging quandaries. In a pioneering investigation, Sinha and his cadre of researchers were the pioneers in successfully training deep neural networks (DNN) to address inverse problems in computational imaging, illuminating its potential in lensless imaging .…”
Section: Introductionmentioning
confidence: 99%
“…Public encryption differs from symmetric encryption, It uses the same key for both enciphering & deciphering [8,9]. The primary benefit of using an asym metric encryption key is that it provides strong encryption that makes decryption of the actual text is a challenge and difficult for hackers to predict [15].…”
Section: Introductionmentioning
confidence: 99%
“…The focus on this artefact as a specific lung infection lineage is growing. Broad bands of tissue in the lungs emerge at the pleural line and move forward in a coordinated manner through lung sliding, as described in[6,25].…”
mentioning
confidence: 99%