乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T00:19:14Z","timestamp":1722644354219},"reference-count":37,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T00:00:00Z","timestamp":1657843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62105328"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Low-light images have low contrast and high noise, making them not easily readable. Most existing image-enhancement methods focus on color images. In the present study, an enhancement and denoising algorithm for single low-light grayscale images is proposed. The algorithm is based on the multi-exposure fusion framework. First, on the basis of the low-light tone-mapping operators, the optimal virtual exposure image is constructed according to the information entropy criterion. Then, the latent low-rank representation is applied to two images to generate low-ranking parts and saliency parts to reduce noise after fusion. Next, the initial weight map is constructed based on the information contained in the decomposed images, and an adaptive weight refined algorithm is proposed to restore as much structural information as possible and keep the details while avoiding halo artifacts. When solving the weight maps, the decomposition and optimization of the nonlinear problem is converted into a total variation model, and an iterative method is used to reduce the computational complexity. Last, the normalized weight map is used for image fusion to obtain the enhanced image. The experimental results showed that the proposed method performed well both in the subjective and objective evaluation of state-of-the-art enhancement methods for low-light grayscale images.<\/jats:p>","DOI":"10.3390\/rs14143398","type":"journal-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T05:57:11Z","timestamp":1657864631000},"page":"3398","source":"Crossref","is-referenced-by-count":1,"title":["Enhancement and Noise Suppression of Single Low-Light Grayscale Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Ting","family":"Nie","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]},{"given":"Xiaofeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"Mechanical and Electronic Engineering Department, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Hongxing","family":"Liu","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]},{"given":"Mingxuan","family":"Li","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"Mechanical and Electronic Engineering Department, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Shenkai","family":"Nong","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"Mechanical and Electronic Engineering Department, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Hangfei","family":"Yuan","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]},{"given":"Yuchen","family":"Zhao","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]},{"given":"Liang","family":"Huang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1109\/TCE.2007.381734","article-title":"A dynamic histograme equalization for image contrast enhancement","volume":"53","author":"Kabir","year":"2007","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/30.580378","article-title":"Contrast enhancement using brightness preserving bi- histogram equalization","volume":"43","author":"Kim","year":"1997","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TCE.2003.1261234","article-title":"Minimum mean brightness error bihistogram equalization in contrast enhancement","volume":"49","author":"Chen","year":"2003","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1049\/el.2011.3421","article-title":"Image enhancement using backgroundbrightness preserving histogram equalization","volume":"48","author":"Tan","year":"2012","journal-title":"Electron. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"169196","DOI":"10.1109\/ACCESS.2020.3024095","article-title":"Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer","volume":"8","author":"Ahmed","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"163395","DOI":"10.1109\/ACCESS.2019.2952545","article-title":"Global and Adaptive Contrast Enhancement for Low Illumination Gray Images","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Alavi, M., and Kargari, M. (2020, January 23\u201324). A novel method for contrast enhancement of gray scale image based on shadowed sets. Proceedings of the 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Mashhad, Iran.","DOI":"10.1109\/ICSPIS51611.2020.9349563"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","article-title":"LIME: Low-Light image enhancement viaillumination map estimation","volume":"26","author":"Guo","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"145297","DOI":"10.1109\/ACCESS.2020.3015217","article-title":"Low-Light Image Enhancement with Regularized Illumination Optimization and Deep Noise Suppression","volume":"8","author":"Guo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fu, X., Zeng, D., Huang, Y., Zhang, X.-P., and Ding, X. (2016, January 27\u201330). A weighted variational model for simultaneous re_ectance and illumination estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.304"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/TCSVT.2021.3073371","article-title":"RetinexDIP: A Unified Deep Framework for Low-Light Image Enhancement","volume":"32","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_12","unstructured":"Dong, X., Pang, Y., and Wen, J. (2010, January 11\u201315). Fast efficient algorithm for enhancement of low lighting video. Proceedings of the IEEE International Conference on Multimedia and Expo, Barcelona, Spain."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, L., Wang, R., Wang, W., and Gao, W. (2015, January 27\u201330). A low-light image enhancement method for both denoising and contrast enlarging. Proceedings of the IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351501"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Thepade, S.D., and Shirbhate, A. (2020, January 4\u20136). Visibility Enhancement in Low Light Images with Weighted Fusion of Robust Retinex Model and Dark Channel Prior. Proceedings of the 2020 IEEE Bombay Section Signature Conference (IBSSC), Mumbai, India.","DOI":"10.1109\/IBSSC51096.2020.9332217"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"16736","DOI":"10.1109\/ACCESS.2021.3053607","article-title":"LLISP: Low-Light Image Signal Processing Net via Two-Stage Network","volume":"9","author":"Zhu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"33547","DOI":"10.1109\/ACCESS.2022.3161527","article-title":"LiCENt: Low-Light Image Enhancement Using the Light Channel of HSL","volume":"10","author":"Garg","year":"2022","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7984","DOI":"10.1109\/TIP.2020.3008396","article-title":"Lightening network for low-light image enhancement","volume":"29","author":"Wang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"36766","DOI":"10.1109\/ACCESS.2022.3163241","article-title":"Image Enhancement Algorithm Based on GAN Neural Network","volume":"10","author":"Xu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"169762","DOI":"10.1109\/ACCESS.2019.2954912","article-title":"High-Dynamic Range, Night Vision, Image-Fusion Algorithm Based on a Decomposition Convolution Neural Network","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Dang, L. (2018, January 10\u201312). Adaptive low-gray image enhancement based on BP neural network and improved unsharp maskmethod. Proceedings of the 5th International Conference on Systems and Informatics (ICSAI), Nanjing, China.","DOI":"10.1109\/ICSAI.2018.8599500"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.inffus.2016.05.004","article-title":"Pixel-Level image fusion: A survey of the state of the art","volume":"33","author":"Li","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, Q., Fu, X., Zhang, X.P., and Ding, X. (2016, January 25\u201328). A fusion-based method for single backlit image enhancement. Proceedings of the IEEE International Conference on Image Processing, Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533126"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.sigpro.2016.05.031","article-title":"A Fusion-based Enhancing Method for Weakly Illuminated Images","volume":"129","author":"Fu","year":"2016","journal-title":"Signal Processing"},{"key":"ref_24","unstructured":"Ying, Z., Li, G., and Gao, W. (2017). A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ying, Z., Li, G., Ren, Y., Wang, R., and Wang, W. (2017, January 22\u201324). A new image contrast enhancement algorithm using exposure fusion framework. Proceedings of the International Conference on Computer Analysis of Images and Patterns, Ystad, Sweden.","DOI":"10.1007\/978-3-319-64698-5_4"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7801415","DOI":"10.1109\/JPHOT.2019.2935134","article-title":"Weighted Sparse Representation and Gradient Domain Guided Filter Pyramid Image Fusion Based on Low-Light-Level Dual-Channel Camera","volume":"11","author":"Chen","year":"2019","journal-title":"IEEE Photonics J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1145\/1190036.1190040","article-title":"A reality check for tone-mapping operators","volume":"3","author":"Ashikhmin","year":"2006","journal-title":"ACM Trans. Appl. Percept."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nie, T., Huang, L., Liu, H., Li, X., Zhao, Y., Yuan, H., Song, X., and He, B. (2021). Multi-Exposure Fusion of Gray Images Under Low Illumination Based on Low-Rank Decomposition. Remote Sens., 13.","DOI":"10.3390\/rs13020204"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"56341","DOI":"10.1109\/ACCESS.2019.2913289","article-title":"An Adaptive Two-Scale Image Fusion of Visible and Infrared Images","volume":"7","author":"Han","year":"2019","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1137\/050626090","article-title":"Signal recovery by proximal forward-backward splitting","volume":"4","author":"Combettes","year":"2005","journal-title":"Multiscale Model. Simul."},{"key":"ref_31","first-page":"1","article-title":"Structure extraction from texture viarelative total variation","volume":"31","author":"Xu","year":"2012","journal-title":"ACM Trans. Graph."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1109\/TCYB.2013.2290435","article-title":"Exposure fusion using boostinglaplacian pyramid","volume":"44","author":"Shen","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hu, L., Qin, M., Zhang, F., Du, Z., and Liu, R. (2021). RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13010062"},{"key":"ref_34","first-page":"98117","article-title":"Mean squared error: Love it or leave it?","volume":"26","author":"Wang","year":"2009","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3538","DOI":"10.1109\/TIP.2013.2261309","article-title":"Naturalness preserved enhancement algorithm for non-uniform illumination images","volume":"22","author":"Wang","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u201cCompletely Blind\u201d Image Quality Analyzer","volume":"20","author":"Mittal","year":"2013","journal-title":"IEEE Signal Processing Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3398\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T05:03:42Z","timestamp":1722575022000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,15]]},"references-count":37,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14143398"],"URL":"https:\/\/doi.org\/10.3390\/rs14143398","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,7,15]]}}}