Hoton Mai Girma Mai Sauri da Ƙarfi a Ƙarƙashin Hasken Ƙarami ta Amfani da Na'urori masu Gano Pixel Guda

1. Gabatarwa

Ɗaukar hoto mai sauri sosai a ƙarƙashin yanayin haske mara kyau kalubale ce mai mahimmanci a fagage kamar biophotonics, microfluidics, da kimiyyar kayan aiki. Na'urori na gargajiya masu ɗaukar hoto (CCD/CMOS) suna fuskantar matsala tsakanin sauri da hankali. Wannan takarda ta gabatar da wata hanya mai ban mamaki ta amfani da na'urori masu gano pixel guda tare da haɗa su da tsarin 'ghost imaging' na kwamfuta da jerin fitilun LED na RGB mai sauri don samun hoton bidiyo a saurin 1.4MHz, tare da yuwuwar cikakken saurin firam har zuwa 100MHz, ko da a cikin yanayi mara kyau na haske.

2. Hanyoyin Aiki

2.1. Ka'idar Ɗaukar Hoto ta Pixel Guda

Ɗaukar hoto ta pixel guda (SPI) tana maye gurbin ƙayyadaddun sarari da ma'aunin jeri na lokaci. Wani sanannen tsari na haske yana haskaka wani abu, sannan kuma na'ura mai gano "buket" guda ɗaya, mai matuƙar hankali, tana auna jimlar ƙarfin hasken da aka nuna ko watsa. Ta hanyar haɗa jerin sanannun tsarin haske tare da ma'aunin buket ɗin da suka dace, ana iya sake gina hoton abu ta hanyar lissafi.

2.2. Daidaita Jerin Fitilun LED na RGB

Babban ƙirƙira shine amfani da jerin fitilun LED na RGB na al'ada a matsayin mai daidaita hasken sarari. Wannan jeri na iya canza tsarin haske a saurin microsecond, wanda ya zarce iyawar na'urorin gargajiya na micromirror na dijital (DMDs) ko na'urorin daidaita hasken sarari na ruwa mai ruwa (LC-SLMs), waɗanda ke da iyaka a ƙimar kHz.

2.3. Tsarin 'Ghost Imaging' na Kwamfuta

Tsarin yana amfani da tsarin 'ghost imaging' na kwamfuta (CGI). Tsarin haske an riga an ƙayyade su (misali, tsari na bazuwar ko na Hadamard) kuma algorithm na sake gina ya san su. Alamar mai gano buket ɗin $B_i$ don tsarin $P_i(x,y)$ na $i$-th ana bayar da ita ta: $$B_i = \int\int O(x,y) \cdot P_i(x,y) \, dx\,dy + \text{hargitsi}$$ inda $O(x,y)$ shine yanayin nuna/watsa na abu. Ana sake gina hoton ta hanyar warware matsalar juyawa, sau da yawa ta amfani da fasaha kamar ma'aunin matsi don bayanan da ba a cika auna ba.

3. Cikakkun Bayanai na Fasaha & Tsarin Lissafi

Ana iya tsara sake gina hoton a matsayin matsalar algebra na layi. Bari $\mathbf{b}$ ya zama vector na ma'aunin buket ɗin $M$, $\mathbf{o}$ ya zama hoton pixel $N$ da aka jera, kuma $\mathbf{A}$ ya zama matrix na ma'auni $M \times N$ inda kowane layi ya zama tsarin haske da aka lalata. Ƙirar gaba ita ce: $$\mathbf{b} = \mathbf{A}\mathbf{o} + \mathbf{n}$$ inda $\mathbf{n}$ shine hargitsi. Don $M < N$ (ma'aunin matsi), sake ginawa yana warwarewa: $$\hat{\mathbf{o}} = \arg\min_{\mathbf{o}} \|\mathbf{b} - \mathbf{A}\mathbf{o}\|_2^2 + \lambda \Psi(\mathbf{o})$$ inda $\Psi(\mathbf{o})$ shine mai daidaitawa mai haɓaka ƙarancin (misali, alamar $\ell_1$ a cikin yanki na canzawa kamar wavelet). Amfani da jeri na RGB yana gabatar da irin waɗannan lissafi guda uku (don tashoshi na R, G, B), yana ba da damar ɗaukar hoto mai launi.

4. Sakamakon Gwaji & Bayanai

4.1. Hoton Farfela Mai Sauri

Babban nunin ya haɗa da ɗaukar hoton farfela mai juyawa da sauri. Tsarin ya yi nasara wajen ɗaukar jerin bidiyo masu haske a cikin firam miliyan 1.4 a kowace dakika, yana nuna motsin motsin ruwan wukake wanda ba za a iya gani da na'urorin ɗaukar hoto na yau da kullun masu sauri a ƙarƙashin ƙuntatawa iri ɗaya na haske mara kyau ba. Wannan ya tabbatar da ikon hanyar don abubuwan da ba su maimaita ba, abubuwan da ba a taɓa gani ba masu sauri sosai.

4.2. Aikin Ƙarƙashin Hasken Ƙarami

Ta hanyar haɗa diodes na avalanche na photon guda (SPADs) a matsayin mai gano buket ɗin, ingantaccen gano tsarin ya ƙaru sosai. Wannan ya ba da damar sake gina hoto mai haske a ƙarƙashin yanayin ƙarancin photon, yana turawa iyaka don ɗaukar hoto mai sauri a ƙarƙashin haske mara kyau. Fa'idar gine-ginen SPI—tattara duk haske a kan mai gano mai hankali guda ɗaya—an tabbatar da cewa ya fi rarraba ƴan photons a cikin pixels da yawa a cikin CCD/CMOS.

5. Tsarin Nazari & Misalin Lamari

Lamari: Lura da Motsin Kwayoyin Halitta na Wucin Gadi. Yi la'akari da amfani da wannan tsarin SPI don lura da raƙuman ion na calcium a cikin neurons, wani abu mai sauri, mara ƙarfi, kuma ba ya maimaitawa. Na'urar ɗaukar hoto ta sCMOS ta gargajiya na iya buƙatar haske mai ƙarfi, mai lalata don samun alamar da za a iya amfani da ita cikin sauri. Tsarin SPI zai yi aiki kamar haka: 1) Jerin fitilun LED na RGB suna haskaka jerin haske mai sauri, ƙarancin ƙarfi a kan al'adun neuron. 2) SPAD guda ɗaya yana tattara duk photons na fluorescence da aka fitar don amsa. 3) Ta amfani da sanannen jerin tsari da bayanan alamar lokaci na SPAD, ana sake gina bidiyo mai sauri, ƙarancin haske na yaduwar raƙuman calcium ta hanyar lissafi, yana rage lalacewar hoto.

6. Ƙarfi, Iyakoki & Nazari Mai mahimmanci

Babban Fahimta: Wannan aikin ba kawai haɓaka sauri ne kawai ba; canji ne na tsari wanda ke raba saurin ɗaukar hoto daga fasahar mai gano. Ta hanyar matsar da toshewar sauri zuwa jerin LED mai sauƙin ƙima, sun ƙirƙiri hanyar zuwa ɗaukar hoto na MHz wanda ke kaucewa iyakokin asali na kewayen karatun CCD/CMOS da injiniyoyin DMD.

Kwararar Ma'ana: Hujja tana da ban sha'awa: 1) Bukatun sauri suna buƙatar daidaitawa cikin sauri (an warware ta ta LED). 2) Ƙarancin haske yana buƙatar tattara haske matsakaici (an warware ta ta hanyar gano buket). 3) Haɗa su ta hanyar 'ghost imaging' na kwamfuta. Gwajin farfela cikakken tabbataccen shaida ne.

Ƙarfi & Kurakurai: Ƙarfafan suna da girma: samfurin sauri-hankali da ba a taɓa gani ba, ikon launi, da sauƙi na dangi. Kurakurai kuma suna da mahimmanci. Dogaro da sake ginawa na lissafi wani abu ne mai kaifi biyu; yana ba da damar sihiri amma yana gabatar da jinkiri kuma yana buƙatar ƙarfin sarrafa ma'ana don bidiyo na ainihi. Tsarin na yanzu yana da iyakataccen ƙayyadaddun sarari idan aka kwatanta da adadin pixel na na'urori na zamani. Bugu da ƙari, kamar yadda yake tare da duk CGI, aikin yana raguwa tare da motsin yanayin yayin jerin tsari guda ɗaya, kalubale ga abubuwan da suka fi sauri.

Hanyoyin Aiki masu Amfani: Ga masu bincike, wasan nan da nan shine karɓar wannan hanyar jerin LED don kowane aikace-aikace da ya haɗa da abubuwan da ba su da ƙarfi, sauri—tunanin hasken rayuwa, bincike na plasma, ko ɗaukar hoto na quantum. Ga masu haɓakawa, gaba gaba shine ƙirƙirar ASICs na ainihi, marasa jinkiri da aka keɓance don algorithm na sake ginawa don buɗe bidiyo na MHz na ainihi. Ambaton takardar na masu gano photon guda yana da mahimmanci; haɗa wannan tare da sabbin fasahohin haɗin kai na quantum na iya turawa hankali zuwa iyaka ta ƙarshe.

7. Aikace-aikacen Gaba & Jagororin Bincike

• Hoton Lafiya: Ɗaukar hoto na ainihi, ƙarancin lalacewar hoto na motsin kwayoyin halitta, kwararar jini a cikin capillaries, ko aikin jijiyoyi a cikin nama mai rai.
• Binciken Masana'antu: Sa ido kan hanyoyin masana'antu masu sauri (misali, walda na Laser, aikin guntu na micro-fluidic) inda haske ke da kalubale.
• Binciken Kimiyya: Nazarin halayen sinadarai, karyewar kayan aiki, ko kimiyyar plasma a ƙarƙashin yanayin haske mara kyau ko haɗari.
• Jagororin Bincike: 1) Haɓaka ƙayyadaddun sarari ta hanyar ƙirar tsari mai ci gaba da algorithms na sake ginawa. 2) Rage jinkirin lissafi don amsa na ainihi. 3) Tsawaita kewayon bakan fiye da hasken da ake iya gani (UV, IR). 4) Bincika ƙa'idodin haɓaka quantum don ƙarin ƙarancin haske, kamar yadda aka gani a cikin aikin farko akan 'ghost imaging' na quantum.

8. Nassoshi

1. Zhao, W., Chen, H., Yuan, Y., et al. "Ultra-high-speed color imaging with single-pixel detectors under low light level." arXiv:1907.09517 (2019).
2. Shapiro, J. H. "Computational ghost imaging." Physical Review A, 78(6), 061802 (2008).
3. Gibson, G. M., Johnson, S. D., & Padgett, M. J. "Single-pixel imaging 12 years on: a review." Optics Express, 28(19), 28190-28208 (2020).
4. Boyd, R. W., et al. "Quantum ghost imaging through turbulent atmosphere." In Quantum Communications and Quantum Imaging (Vol. 5161, pp. 200-209). SPIE (2004).
5. Cibiyar Ƙididdiga da Fasaha ta Ƙasa (NIST). "Masu Gano Photon Guda." https://www.nist.gov/programs-projects/single-photon-detectors (An ziyarta: Yana ba da mahallin fasahar SPAD).
6. Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. "Image-to-image translation with conditional adversarial networks." Proceedings of the IEEE conference on computer vision and pattern recognition (2017). (An ambata a matsayin misali na ingantaccen tsarin ɗaukar hoto/sarrafa kwamfuta).