Chetan S. Negi, Koyel Mandal, R. R. Sahay, M. Kankanhalli
{"title":"超分辨率去栅栏:使用视频同时去除围栏和高分辨率图像恢复","authors":"Chetan S. Negi, Koyel Mandal, R. R. Sahay, M. Kankanhalli","doi":"10.1109/ICMEW.2014.6890641","DOIUrl":null,"url":null,"abstract":"In real-world scenarios, images or videos taken at public places using inexpensive low-resolution cameras, such as smartphones are also often degraded by the presence of occlusions such as fences/barricades. Finer details in images captured using such low-end equipment are lost due to blurring and under-sampling. Compounding this problem is missing data due to the presence of an intervening occlusion between the scene and the camera such as a fence. To recover a fence-free high-resolution image, we use videos of the scene captured by panning a hand-held camera and model the effects of various degradations. Initially, we obtain the spatial locations of the fence/occlusions and the global shifts of the degraded background image. The underlying high-resolution fence-free image is modeled as a discontinuity-adaptive Markov random field and its maximum a-posteriori estimate is obtained using an optimization approach. The advantage of using this prior is that high-frequency information is preserved during the reconstruction of the super-resolved image. Specifically, we use the fast graduated non-convexity algorithm to minimize a non-convex energy function. Experiments with both synthetic and real-world data demonstrate the efficacy of the proposed algorithm.","PeriodicalId":178700,"journal":{"name":"2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Super-resolution de-fencing: Simultaneous fence removal and high-resolution image recovery using videos\",\"authors\":\"Chetan S. Negi, Koyel Mandal, R. R. Sahay, M. Kankanhalli\",\"doi\":\"10.1109/ICMEW.2014.6890641\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In real-world scenarios, images or videos taken at public places using inexpensive low-resolution cameras, such as smartphones are also often degraded by the presence of occlusions such as fences/barricades. Finer details in images captured using such low-end equipment are lost due to blurring and under-sampling. Compounding this problem is missing data due to the presence of an intervening occlusion between the scene and the camera such as a fence. To recover a fence-free high-resolution image, we use videos of the scene captured by panning a hand-held camera and model the effects of various degradations. Initially, we obtain the spatial locations of the fence/occlusions and the global shifts of the degraded background image. The underlying high-resolution fence-free image is modeled as a discontinuity-adaptive Markov random field and its maximum a-posteriori estimate is obtained using an optimization approach. The advantage of using this prior is that high-frequency information is preserved during the reconstruction of the super-resolved image. Specifically, we use the fast graduated non-convexity algorithm to minimize a non-convex energy function. Experiments with both synthetic and real-world data demonstrate the efficacy of the proposed algorithm.\",\"PeriodicalId\":178700,\"journal\":{\"name\":\"2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)\",\"volume\":\"104 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMEW.2014.6890641\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMEW.2014.6890641","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Super-resolution de-fencing: Simultaneous fence removal and high-resolution image recovery using videos
In real-world scenarios, images or videos taken at public places using inexpensive low-resolution cameras, such as smartphones are also often degraded by the presence of occlusions such as fences/barricades. Finer details in images captured using such low-end equipment are lost due to blurring and under-sampling. Compounding this problem is missing data due to the presence of an intervening occlusion between the scene and the camera such as a fence. To recover a fence-free high-resolution image, we use videos of the scene captured by panning a hand-held camera and model the effects of various degradations. Initially, we obtain the spatial locations of the fence/occlusions and the global shifts of the degraded background image. The underlying high-resolution fence-free image is modeled as a discontinuity-adaptive Markov random field and its maximum a-posteriori estimate is obtained using an optimization approach. The advantage of using this prior is that high-frequency information is preserved during the reconstruction of the super-resolved image. Specifically, we use the fast graduated non-convexity algorithm to minimize a non-convex energy function. Experiments with both synthetic and real-world data demonstrate the efficacy of the proposed algorithm.