{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:11:09Z","timestamp":1778857869148,"version":"3.51.4"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"25","license":[{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-20285-w","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T06:06:07Z","timestamp":1728367567000},"page":"29143-29158","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Low-light image enhancement using retinex based an extended ResNet model"],"prefix":"10.1007","volume":"84","author":[{"given":"V. S.","family":"Anila","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G.","family":"Nagarajan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"T.","family":"Perarasi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"issue":"11","key":"20285_CR1","doi-asserted-by":"publisher","first-page":"5679","DOI":"10.1109\/TIP.2019.2922106","volume":"28","author":"Y-F Wang","year":"2019","unstructured":"Wang Y-F, Liu H-M, Fu Z-W (2019) Low-Light Image Enhancement via the Absorption Light Scattering Model. IEEE Trans Image Process 28(11):5679\u20135690. https:\/\/doi.org\/10.1109\/TIP.2019.2922106","journal-title":"IEEE Trans Image Process"},{"key":"20285_CR2","doi-asserted-by":"publisher","unstructured":"Anila VS, Nagarajan G, Perarasi T (2024) An exploration of state-of-art approaches on low-light image enhancement techniques. In: Shetty NR, Prasad NH, Nagaraj HC (eds) Advances in communication and applications. ERCICA 2023. Lecture notes in electrical engineering, vol 1105. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-99-7633-1_15","DOI":"10.1007\/978-981-99-7633-1_15"},{"key":"20285_CR3","doi-asserted-by":"publisher","first-page":"129150","DOI":"10.1109\/ACCESS.2019.2940452","volume":"7","author":"W Kim","year":"2019","unstructured":"Kim W, Lee R, Park M, Lee S (2019) Low-Light Image Enhancement Based on Maximal Diffusion Values. IEEE Access 7:129150\u2013129163. https:\/\/doi.org\/10.1109\/ACCESS.2019.2940452","journal-title":"IEEE Access"},{"issue":"18","key":"20285_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s23187763","volume":"23","author":"Z Tian","year":"2023","unstructured":"Tian Z et al (2023) A survey of deep learning-based low-light image enhancement. Sensors 23(18):1\u201322. https:\/\/doi.org\/10.3390\/s23187763","journal-title":"Sensors"},{"key":"20285_CR5","doi-asserted-by":"crossref","unstructured":"Huang SC, Cheng FC, Chiu YS (2013) Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans Image Process 22(3): 1032 1041","DOI":"10.1109\/TIP.2012.2226047"},{"key":"20285_CR6","doi-asserted-by":"publisher","unstructured":"Ueda Y, Moriyama D, Koga T, Suetake N (2020) Histogram Specification -Based Image Enhancement for Backlit Image. In 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, pp. 958-962. https:\/\/doi.org\/10.1109\/ICIP40778.2020.9190929","DOI":"10.1109\/ICIP40778.2020.9190929"},{"key":"20285_CR7","doi-asserted-by":"publisher","first-page":"169887","DOI":"10.1109\/ACCESS.2020.3023485","volume":"8","author":"C Li","year":"2020","unstructured":"Li C, Tang S, Yan J, Zhou T (2020) Low-Light Image Enhancement via Pair of Complementary Gamma Functions by Fusion. IEEE Access 8:169887\u2013169896. https:\/\/doi.org\/10.1109\/ACCESS.2020.3023485","journal-title":"IEEE Access"},{"key":"20285_CR8","doi-asserted-by":"publisher","unstructured":"Z. Gu, C. Chen, and D. Zhang (2018) A low-light image enhancement method based on image degradation model and pure pixel ratio prior. Math Probl Eng 2018. https:\/\/doi.org\/10.1155\/2018\/8178109","DOI":"10.1155\/2018\/8178109"},{"key":"20285_CR9","doi-asserted-by":"crossref","unstructured":"Dhara SK, Sen D (2018) Low light image enhancement using grover S algorithm on superposed luminance levels sobhan kanti dhara and debashis sen department of electronics and electrical communication engineering Indian Institute of Technology, Kharagpur, India. 2018 25th IEEE Int. Conf. Image Process., pp. 1113\u20131117","DOI":"10.1109\/ICIP.2018.8451651"},{"key":"20285_CR10","doi-asserted-by":"publisher","unstructured":"Enhancement I, Yu Y, Adversarial CG, Li H, Cheng J (2021) MSIGEN\u202f: multi-scale illumination-guided low- light image enhancement network MSIGEN\u202f: Multi-scale illumination-guided low-light image enhancement network. https:\/\/doi.org\/10.1088\/1742-6596\/1848\/1\/012085","DOI":"10.1088\/1742-6596\/1848\/1\/012085"},{"issue":"6","key":"20285_CR11","doi-asserted-by":"publisher","first-page":"2828","DOI":"10.1109\/TIP.2018.2810539","volume":"27","author":"M Li","year":"2018","unstructured":"Li M, Liu J, Yang W, Sun X, Guo Z (2018) Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model. IEEE Trans on Image Process 27(6):2828\u20132841. https:\/\/doi.org\/10.1109\/TIP.2018.2810539","journal-title":"IEEE Trans on Image Process"},{"key":"20285_CR12","doi-asserted-by":"publisher","first-page":"178685","DOI":"10.1109\/ACCESS.2019.2958078","volume":"7","author":"C Dai","year":"2019","unstructured":"Dai C, Lin M, Wang J, Hu X (2019) Dual-purpose method for underwater and low-light image enhancement via image layer separation. IEEE Access 7:178685\u2013178698. https:\/\/doi.org\/10.1109\/ACCESS.2019.2958078","journal-title":"IEEE Access"},{"key":"20285_CR13","doi-asserted-by":"publisher","unstructured":"Chongyi Li, Jichang Guo, Fatih Porikli, Yanwei Pang (2018) LightenNet: A convolutional neural network for weakly illuminated image enhancement. Pattern Recognit Lett 104: 15-22. https:\/\/doi.org\/10.1016\/j.patrec.2018.01.010.","DOI":"10.1016\/j.patrec.2018.01.010"},{"key":"20285_CR14","doi-asserted-by":"publisher","unstructured":"X. Xu et al (2022) ColorPolarNet: residual dense network-based chromatic intensity-polarization imaging in low-light environment. IEEE Trans Instrum Meas 71: 1-10. https:\/\/doi.org\/10.1109\/TIM.2022.3216391","DOI":"10.1109\/TIM.2022.3216391"},{"key":"20285_CR15","doi-asserted-by":"publisher","unstructured":"Peng Y, Chen Z (2020) Application of deep residual neural network to water meter reading recognition. 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). Dalian, China, pp. 774-777. https:\/\/doi.org\/10.1109\/ICAICA50127.2020.9182460.","DOI":"10.1109\/ICAICA50127.2020.9182460"},{"issue":"2","key":"20285_CR16","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","volume":"26","author":"X Guo","year":"2017","unstructured":"Guo X, Li Y, Ling H (2017) LIME: Low-Light Image Enhancement via Illumination Map Estimation. IEEE Transactions on Image Processing 26(2):982\u2013993. https:\/\/doi.org\/10.1109\/TIP.2016.2639450","journal-title":"IEEE Transactions on Image Processing"},{"key":"20285_CR17","doi-asserted-by":"publisher","first-page":"56539","DOI":"10.1109\/ACCESS.2021.3072331","volume":"9","author":"T Ma","year":"2021","unstructured":"Ma T et al (2021) RetinexGAN: unsupervised low-light enhancement with two-layer convolutional decomposition networks. IEEE Access 9:56539\u201356550. https:\/\/doi.org\/10.1109\/ACCESS.2021.3072331","journal-title":"IEEE Access"},{"key":"20285_CR18","doi-asserted-by":"publisher","first-page":"74306","DOI":"10.1109\/ACCESS.2020.2988767","volume":"8","author":"W Huang","year":"2020","unstructured":"Huang W, Zhu Y, Huang R (2020) Low light image enhancement network with attention mechanism and retinex model. IEEE Access 8:74306\u201374314. https:\/\/doi.org\/10.1109\/ACCESS.2020.2988767","journal-title":"IEEE Access"},{"key":"20285_CR19","doi-asserted-by":"publisher","unstructured":"Mehta S, Paunwala C, Vaidya B (2019) CNN based traffic sign classification using Adam Optimizer. 2019 International conference on intelligent computing and control systems (ICCS), Madurai, India, pp. 1293-1298. https:\/\/doi.org\/10.1109\/ICCS45141.2019.9065537.","DOI":"10.1109\/ICCS45141.2019.9065537"},{"key":"20285_CR20","doi-asserted-by":"publisher","unstructured":"\u015een SY, \u00d6zkurt N (2020) convolutional neural network hyperparameter tuning with adam optimizer for ECG classification. 2020 Innovations in intelligent systems and applications conference (ASYU), Istanbul, Turkey. https:\/\/doi.org\/10.1109\/ASYU50717.2020.9259896","DOI":"10.1109\/ASYU50717.2020.9259896"},{"key":"20285_CR21","doi-asserted-by":"publisher","unstructured":"Poojary R, Pai A (2019) comparative study of model optimization techniques in fine-tuned CNN Models. 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA), Ras Al Khaimah, United Arab Emirates, pp. 1-4. https:\/\/doi.org\/10.1109\/ICECTA48151.2019.8959681","DOI":"10.1109\/ICECTA48151.2019.8959681"},{"key":"20285_CR22","doi-asserted-by":"publisher","unstructured":"Amaral T, Silva LM, Alexandre LA, Kandaswamy C, Santos JM de S\u00e1 JM (2013) Using different cost functions to train stacked auto-encoders. 12th Mexican international conference on artificial intelligence, pp. 114-120. https:\/\/doi.org\/10.1109\/MICAI.2013.20","DOI":"10.1109\/MICAI.2013.20"},{"issue":"9","key":"20285_CR23","doi-asserted-by":"publisher","first-page":"5260","DOI":"10.3390\/app13095260","volume":"13","author":"L Zhang","year":"2023","unstructured":"Zhang L, Bian Y, Jiang P, Zhang F (2023) A transfer residual neural network based on resnet-50 for detection of steel surface defects. Applied Sci 13(9):5260. https:\/\/doi.org\/10.3390\/app13095260","journal-title":"Applied Sci"},{"key":"20285_CR24","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L, Zhang J, Humaidi AJ et al (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8:53. https:\/\/doi.org\/10.1186\/s40537-021-00444-8","journal-title":"J Big Data"},{"key":"20285_CR25","doi-asserted-by":"publisher","unstructured":"Cai B, Xu X, Guo K, Jia K, Hu B, Tao D (2017) A Joint Intrinsic-Extrinsic Prior Model for Retinex. 2017 IEEE international conference on computer vision (ICCV), Venice, Italy, pp. 4020-4029. https:\/\/doi.org\/10.1109\/ICCV.2017.431","DOI":"10.1109\/ICCV.2017.431"},{"key":"20285_CR26","doi-asserted-by":"publisher","unstructured":"Wang R, Jiang B, Yang C, Li Q, Zhang B (2022) MAGAN: unsupervised low-light image enhancement guided by mixed-attention. Big Data Min Anal 5(2):110-119. https:\/\/doi.org\/10.26599\/BDMA.2021.9020020.","DOI":"10.26599\/BDMA.2021.9020020"},{"key":"20285_CR27","doi-asserted-by":"crossref","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process 13(4):600-612","DOI":"10.1109\/TIP.2003.819861"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20285-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-20285-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20285-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T11:01:52Z","timestamp":1753354912000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-20285-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,8]]},"references-count":27,"journal-issue":{"issue":"25","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["20285"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-20285-w","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,8]]},"assertion":[{"value":"5 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No conflict of Interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}