{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T16:49:00Z","timestamp":1772038140627,"version":"3.50.1"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62001146"],"award-info":[{"award-number":["62001146"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271180"],"award-info":[{"award-number":["62271180"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00521-023-08852-y","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T15:49:10Z","timestamp":1690386550000},"page":"20869-20887","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Depth-guided deep filtering network for efficient single image bokeh rendering"],"prefix":"10.1007","volume":"35","author":[{"given":"Quan","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8788-1725","authenticated-orcid":false,"given":"Bolun","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Xiaofei","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Aiai","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yaoqi","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Chuqiao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Chenggang","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Shanxin","family":"Yuan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,26]]},"reference":[{"key":"8852_CR1","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.neucom.2021.02.014","volume":"441","author":"W Gan","year":"2021","unstructured":"Gan W, Wong PK, Yu G, Zhao R, Vong CM (2021) Light-weight network for real-time adaptive stereo depth estimation. Neurocomputing 441:118\u2013127","journal-title":"Neurocomputing"},{"key":"8852_CR2","doi-asserted-by":"crossref","unstructured":"Poggi M, Pallotti D, Tosi F, Mattoccia S (2019) Guided stereo matching. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 979\u2013988","DOI":"10.1109\/CVPR.2019.00107"},{"key":"8852_CR3","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.neucom.2015.09.109","volume":"178","author":"Z Li","year":"2016","unstructured":"Li Z, Wang K, Meng D, Xu C (2016) Multi-view stereo via depth map fusion: a coordinate decent optimization method. Neurocomputing 178:46\u201361","journal-title":"Neurocomputing"},{"key":"8852_CR4","doi-asserted-by":"crossref","unstructured":"Luo C, Li Y, Lin K, Chen G, Lee S-J, Choi J, Yoo YF, Polley MO (2020) Wavelet synthesis net for disparity estimation to synthesize dslr calibre bokeh effect on smartphones. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2407\u20132415","DOI":"10.1109\/CVPR42600.2020.00248"},{"key":"8852_CR5","doi-asserted-by":"crossref","unstructured":"Yang M, Wu F, Li W (2020) Waveletstereo: learning wavelet coefficients of disparity map in stereo matching. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 12885\u201312894","DOI":"10.1109\/CVPR42600.2020.01290"},{"issue":"4","key":"8852_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3197517.3201329","volume":"37","author":"N Wadhwa","year":"2018","unstructured":"Wadhwa N, Garg R, Jacobs DE, Feldman BE, Kanazawa N, Carroll R, Movshovitz-Attias Y, Barron JT, Pritch Y, Levoy M (2018) Synthetic depth-of-field with a single-camera mobile phone. ACM Trans Graph (ToG) 37(4):1\u201313","journal-title":"ACM Trans Graph (ToG)"},{"key":"8852_CR7","doi-asserted-by":"crossref","unstructured":"Liu D, Nicolescu R, Klette R (2015) Bokeh effects based on stereo vision. In: International conference on computer analysis of images and patterns. Springer, pp 198\u2013210","DOI":"10.1007\/978-3-319-23192-1_17"},{"key":"8852_CR8","doi-asserted-by":"crossref","unstructured":"Ignatov A, Timofte R, Kulik A, Yang S, Wang K, Baum F, Wu M, Xu L, Van\u00a0Gool L (2019) Ai benchmark: all about deep learning on smartphones in 2019. In: 2019 IEEE\/CVF international conference on computer vision workshop (ICCVW). IEEE, pp 3617\u20133635","DOI":"10.1109\/ICCVW.2019.00447"},{"key":"8852_CR9","doi-asserted-by":"crossref","unstructured":"Ignatov A, Patel J, Timofte R (2020) Rendering natural camera bokeh effect with deep learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp 418\u2013419","DOI":"10.1109\/CVPRW50498.2020.00217"},{"key":"8852_CR10","doi-asserted-by":"crossref","unstructured":"Qian M, Qiao C, Lin J, Guo Z, Li C, Leng C, Cheng J (2020) Bggan: bokeh-glass generative adversarial network for rendering realistic bokeh. In: European conference on computer vision. Springer, pp 229\u2013244","DOI":"10.1007\/978-3-030-67070-2_14"},{"key":"8852_CR11","doi-asserted-by":"crossref","unstructured":"Ignatov A, Timofte R, Qian M, Qiao C, Lin J, Guo Z, Li C, Leng C, Cheng J, Peng J et\u00a0al (2020) Aim 2020 challenge on rendering realistic bokeh. In: European conference on computer vision. Springer, pp 213\u2013228","DOI":"10.1007\/978-3-030-67070-2_13"},{"key":"8852_CR12","doi-asserted-by":"crossref","unstructured":"Purohit K, Suin M, Kandula P, Ambasamudram R. (2019) Depth-guided dense dynamic filtering network for bokeh effect rendering. In: 2019 IEEE\/CVF international conference on computer vision workshop (ICCVW), pp 3417\u20133426","DOI":"10.1109\/ICCVW.2019.00424"},{"key":"8852_CR13","doi-asserted-by":"crossref","unstructured":"Li Z, Snavely N (2018) Megadepth: learning single-view depth prediction from internet photos. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2041\u20132050","DOI":"10.1109\/CVPR.2018.00218"},{"key":"8852_CR14","doi-asserted-by":"crossref","unstructured":"Hou Q, Cheng M-M, Hu X, Borji A, Tu Z, Torr PH (2017) Deeply supervised salient object detection with short connections. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3203\u20133212","DOI":"10.1109\/CVPR.2017.563"},{"key":"8852_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2021.103089","volume":"77","author":"S Dutta","year":"2021","unstructured":"Dutta S (2021) Depth-aware blending of smoothed images for bokeh effect generation. J Vis Commun Image Represent 77:103089","journal-title":"J Vis Commun Image Represent"},{"key":"8852_CR16","unstructured":"Huang Y, Juefei-Xu F, Guo Q, Miao W, Liu Y, Pu G (2021) Advbokeh: learning to adversarially defocus blur. arXiv:2111.12971"},{"issue":"5","key":"8852_CR17","doi-asserted-by":"publisher","first-page":"1815","DOI":"10.3390\/s21051815","volume":"21","author":"K Xian","year":"2021","unstructured":"Xian K, Peng J, Zhang C, Lu H, Cao Z (2021) Ranking-based salient object detection and depth prediction for shallow depth-of-field. Sensors 21(5):1815","journal-title":"Sensors"},{"key":"8852_CR18","doi-asserted-by":"crossref","unstructured":"Lee J, Son H, Rim J, Cho S, Lee S (2021) Iterative filter adaptive network for single image defocus deblurring. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2034\u20132042","DOI":"10.1109\/CVPR46437.2021.00207"},{"issue":"1","key":"8852_CR19","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1109\/TCYB.2022.3163152","volume":"53","author":"X Zhou","year":"2023","unstructured":"Zhou X, Shen K, Weng L, Cong R, Zheng B, Zhang J, Yan C (2023) Edge-guided recurrent positioning network for salient object detection in optical remote sensing images. IEEE Trans Cybern 53(1):539\u2013552","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"8852_CR20","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/TMM.2012.2225034","volume":"15","author":"N Imamoglu","year":"2013","unstructured":"Imamoglu N, Lin W, Fang Y (2013) A saliency detection model using low-level features based on wavelet transform. IEEE Trans Multimed 15(1):96\u2013105","journal-title":"IEEE Trans Multimed"},{"key":"8852_CR21","doi-asserted-by":"crossref","unstructured":"Tu Z, Ma Y, Li Z, Li C, Xu J, Liu Y (2022) Rgbt salient object detection: a large-scale dataset and benchmark. IEEE Trans Multimed","DOI":"10.1109\/TMM.2022.3171688"},{"key":"8852_CR22","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1109\/TMM.2020.2997192","volume":"23","author":"J Li","year":"2020","unstructured":"Li J, Pan Z, Liu Q, Wang Z (2020) Stacked u-shape network with channel-wise attention for salient object detection. IEEE Trans Multimed 23:1397\u20131409","journal-title":"IEEE Trans Multimed"},{"key":"8852_CR23","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1109\/TMM.2021.3063916","volume":"24","author":"C Mou","year":"2021","unstructured":"Mou C, Zhang J, Fan X, Liu H, Wang R (2021) Cola-net: collaborative attention network for image restoration. IEEE Trans Multimed 24:1366\u20131377","journal-title":"IEEE Trans Multimed"},{"key":"8852_CR24","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.1109\/TCI.2020.2996075","volume":"6","author":"J Jiang","year":"2020","unstructured":"Jiang J, Sun H, Liu X, Ma J (2020) Learning spatial\u2013spectral prior for super-resolution of hyperspectral imagery. IEEE Trans Comput Imaging 6:1082\u20131096","journal-title":"IEEE Trans Comput Imaging"},{"key":"8852_CR25","doi-asserted-by":"crossref","unstructured":"Lin C, Rong X, Yu X (2022) Msaff-net: multiscale attention feature fusion networks for single image dehazing and beyond. IEEE Trans Multimed","DOI":"10.1109\/TMM.2022.3155937"},{"key":"8852_CR26","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1109\/TMM.2021.3134172","volume":"25","author":"K Park","year":"2023","unstructured":"Park K, Soh JW, Cho NI (2023) A dynamic residual self-attention network for lightweight single image super-resolution. IEEE Trans Multimed 25:907\u2013918","journal-title":"IEEE Trans Multimed"},{"issue":"10","key":"8852_CR27","doi-asserted-by":"publisher","first-page":"10556","DOI":"10.1109\/TCYB.2021.3064571","volume":"52","author":"X Liu","year":"2021","unstructured":"Liu X, Li L, Liu F, Hou B, Yang S, Jiao L (2021) Gafnet: group attention fusion network for pan and ms image high-resolution classification. IEEE Trans Cybern 52(10):10556\u201310569","journal-title":"IEEE Trans Cybern"},{"issue":"6","key":"8852_CR28","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1109\/TMM.2017.2648498","volume":"19","author":"B Zhao","year":"2017","unstructured":"Zhao B, Wu X, Feng J, Peng Q, Yan S (2017) Diversified visual attention networks for fine-grained object classification. IEEE Trans Multimed 19(6):1245\u20131256","journal-title":"IEEE Trans Multimed"},{"key":"8852_CR29","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TMM.2021.3120873","volume":"25","author":"X Lin","year":"2023","unstructured":"Lin X, Sun S, Huang W, Sheng B, Li P, Feng DD (2023) Eapt: efficient attention pyramid transformer for image processing. IEEE Trans Multimed 25:50\u201361","journal-title":"IEEE Trans Multimed"},{"issue":"8","key":"8852_CR30","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1109\/TMM.2019.2894964","volume":"21","author":"F Lyu","year":"2019","unstructured":"Lyu F, Wu Q, Hu F, Wu Q, Tan M (2019) Attend and imagine: multi-label image classification with visual attention and recurrent neural networks. IEEE Trans Multimed 21(8):1971\u20131981","journal-title":"IEEE Trans Multimed"},{"key":"8852_CR31","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"8852_CR32","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"8852_CR33","unstructured":"Zhang Y, Li K, Li K, Zhong B, Fu Y (2019) Residual non-local attention networks for image restoration. arXiv:1903.10082"},{"key":"8852_CR34","doi-asserted-by":"crossref","unstructured":"Mei Y, Fan Y, Zhou Y (2021) Image super-resolution with non-local sparse attention. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3517\u20133526","DOI":"10.1109\/CVPR46437.2021.00352"},{"key":"8852_CR35","unstructured":"Jia X, Brabandere BD, Tuytelaars T, Gool LV (2016) Dynamic filter networks. In: International conference on neural information processing systems, vol 29"},{"key":"8852_CR36","unstructured":"Wu J, Li D, Yang Y, Bajaj C, Ji X (2018) Dynamic sampling convolutional neural networks. arXiv:1803.07624"},{"key":"8852_CR37","doi-asserted-by":"crossref","unstructured":"Mildenhall B, Barron JT, Chen J, Sharlet D, Ng R, Carroll R (2018) Burst denoising with kernel prediction networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2502\u20132510","DOI":"10.1109\/CVPR.2018.00265"},{"key":"8852_CR38","doi-asserted-by":"crossref","unstructured":"He J, Deng Z, Qiao Y (2019) Dynamic multi-scale filters for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3562\u20133572","DOI":"10.1109\/ICCV.2019.00366"},{"issue":"11","key":"8852_CR39","doi-asserted-by":"publisher","first-page":"3982","DOI":"10.1109\/TCSVT.2019.2931045","volume":"30","author":"B Zheng","year":"2019","unstructured":"Zheng B, Chen Y, Tian X, Zhou F, Liu X (2019) Implicit dual-domain convolutional network for robust color image compression artifact reduction. IEEE Trans Circuits Syst Video Technol 30(11):3982\u20133994","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"8852_CR40","unstructured":"Kong S, Fowlkes C(2019) Multigrid predictive filter flow for unsupervised learning on videos. arXiv:1904.01693"},{"key":"8852_CR41","doi-asserted-by":"crossref","unstructured":"Zhou S, Zhang J, Pan J, Xie H, Zuo W, Ren J (2019) Spatio-temporal filter adaptive network for video deblurring. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2482\u20132491","DOI":"10.1109\/ICCV.2019.00257"},{"issue":"7","key":"8852_CR42","doi-asserted-by":"publisher","first-page":"4138","DOI":"10.1109\/TCSVT.2021.3123621","volume":"32","author":"H Zhao","year":"2021","unstructured":"Zhao H, Zheng B, Yuan S, Zhang H, Yan C, Li L, Slabaugh G (2021) Cbren: convolutional neural networks for constant bit rate video quality enhancement. IEEE Trans Circuits Syst Video Technol 32(7):4138\u20134149","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"8852_CR43","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1109\/TCI.2022.3171417","volume":"8","author":"B Zheng","year":"2022","unstructured":"Zheng B, Chen Q, Yuan S, Zhou X, Zhang H, Zhang J, Yan C, Slabaugh G (2022) Constrained predictive filters for single image bokeh rendering. IEEE Trans Comput Imaging 8:346\u2013357","journal-title":"IEEE Trans Comput Imaging"},{"key":"8852_CR44","doi-asserted-by":"crossref","unstructured":"Barron JT, Adams A, Shih Y, Hern\u00e1ndez C (2015) Fast bilateral-space stereo for synthetic defocus. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4466\u20134474","DOI":"10.1109\/CVPR.2015.7299076"},{"key":"8852_CR45","doi-asserted-by":"crossref","unstructured":"Busam B, Hog M, McDonagh S, Slabaugh G (2019) Sterefo: efficient image refocusing with stereo vision. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops","DOI":"10.1109\/ICCVW.2019.00411"},{"issue":"3","key":"8852_CR46","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1109\/TVCG.2008.106","volume":"15","author":"S Lee","year":"2009","unstructured":"Lee S, Kim GJ, Choi S (2009) Real-time depth-of-field rendering using anisotropically filtered mipmap interpolation. IEEE Trans Vis Comput Graph 15(3):453\u2013464","journal-title":"IEEE Trans Vis Comput Graph"},{"issue":"8","key":"8852_CR47","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1007\/s00138-016-0775-5","volume":"27","author":"D Liu","year":"2016","unstructured":"Liu D, Nicolescu R, Klette R (2016) Stereo-based bokeh effects for photography. Mach Vis Appl 27(8):1325\u20131337","journal-title":"Mach Vis Appl"},{"key":"8852_CR48","first-page":"529","volume":"9","author":"G Riguer","year":"2004","unstructured":"Riguer G, Tatarchuk N, Isidoro J (2004) Real-time depth of field simulation. ShaderX2 Shader Program Tips Tricks DirectX 9:529\u2013556","journal-title":"ShaderX2 Shader Program Tips Tricks DirectX"},{"key":"8852_CR49","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.1111\/j.1467-8659.2008.01344.x","volume":"27","author":"S Lee","year":"2008","unstructured":"Lee S, Kim GJ, Choi S (2008) Real-time depth-of-field rendering using point splatting on per-pixel layers. Comput Graph Forum 27:1955\u20131962","journal-title":"Comput Graph Forum"},{"key":"8852_CR50","doi-asserted-by":"crossref","unstructured":"Dutta S, Das SD, Shah NA, Tiwari AK (2021) Stacked deep multi-scale hierarchical network for fast bokeh effect rendering from a single image. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR) workshops, pp 2398\u20132407","DOI":"10.1109\/CVPRW53098.2021.00272"},{"key":"8852_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2022.103580","volume":"87","author":"Z Wang","year":"2022","unstructured":"Wang Z, Jiang A, Zhang C, Li H, Liu B (2022) Self-supervised multi-scale pyramid fusion networks for realistic bokeh effect rendering. J Vis Commun Image Represent 87:103580","journal-title":"J Vis Commun Image Represent"},{"key":"8852_CR52","doi-asserted-by":"crossref","unstructured":"Georgiadis K, Sa\u00e0-Garriga A, Yucel MK, Drosou A, Manganelli B (2023) Adaptive mask-based pyramid network for realistic bokeh rendering. In: Computer vision-ECCV 2022 workshops: Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part II. Springer, pp 429\u2013444","DOI":"10.1007\/978-3-031-25063-7_26"},{"key":"8852_CR53","doi-asserted-by":"crossref","unstructured":"Ignatov A, Timofte R, Zhang J, Zhang F, Yu G, Ma Z, Wang H, Kwon M, Qian H, Tong W et\u00a0al(2023) Realistic bokeh effect rendering on mobile gpus, mobile ai & aim 2022 challenge: report. In: Computer vision-ECCV 2022 workshops: Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part III. Springer, pp 153\u2013173","DOI":"10.1007\/978-3-031-25066-8_7"},{"key":"8852_CR54","doi-asserted-by":"crossref","unstructured":"Ignatov A, Patel J, Timofte R, Zheng B, Ye X, Huang L, Tian X, Dutta S, Purohit K, Kandula P et\u00a0al(2019) Aim 2019 challenge on bokeh effect synthesis: methods and results. In: 2019 IEEE\/CVF international conference on computer vision workshop (ICCVW). IEEE, pp 3591\u20133598","DOI":"10.1109\/ICCVW.2019.00444"},{"key":"8852_CR55","doi-asserted-by":"crossref","unstructured":"Luo X, Peng J, Xian K, Wu Z, Cao Z (2020) Bokeh rendering from defocus estimation. In: European conference on computer vision. Springer, pp 245\u2013261","DOI":"10.1007\/978-3-030-67070-2_15"},{"key":"8852_CR56","doi-asserted-by":"crossref","unstructured":"Peng J, Cao Z, Luo X, Lu H, Xian K, Zhang J (2022) Bokehme: when neural rendering meets classical rendering. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 16283\u201316292","DOI":"10.1109\/CVPR52688.2022.01580"},{"key":"8852_CR57","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.inffus.2022.08.023","volume":"89","author":"X Luo","year":"2023","unstructured":"Luo X, Peng J, Xian K, Wu Z, Cao Z (2023) Defocus to focus: photo-realistic bokeh rendering by fusing defocus and radiance priors. Inf Fusion 89:320\u2013335","journal-title":"Inf Fusion"},{"key":"8852_CR58","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"8852_CR59","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted Intervention. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"8852_CR60","doi-asserted-by":"crossref","unstructured":"Zheng B, Yuan S, Slabaugh G, Leonardis A (2020) Image demoireing with learnable bandpass filters. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3636\u20133645","DOI":"10.1109\/CVPR42600.2020.00369"},{"issue":"11","key":"8852_CR61","doi-asserted-by":"publisher","first-page":"7705","DOI":"10.1109\/TPAMI.2021.3115139","volume":"44","author":"B Zheng","year":"2021","unstructured":"Zheng B, Yuan S, Yan C, Tian X, Zhang J, Sun Y, Liu L, Leonardis A, Slabaugh G (2021) Learning frequency domain priors for image demoireing. IEEE Trans Pattern Anal Mach Intell 44(11):7705\u20137717","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8852_CR62","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"issue":"4","key":"8852_CR63","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","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\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"8852_CR64","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA, Shechtman E, Wang O (2018) The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 586\u2013595","DOI":"10.1109\/CVPR.2018.00068"},{"key":"8852_CR65","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"8852_CR66","first-page":"8026","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L et al (2019) Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32:8026\u20138037","journal-title":"Adv Neural Inf Process Syst"},{"key":"8852_CR67","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Mu\u00a0Lee K (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 136\u2013144","DOI":"10.1109\/CVPRW.2017.151"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08852-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08852-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08852-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:21:00Z","timestamp":1693354860000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08852-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,26]]},"references-count":67,"journal-issue":{"issue":"28","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8852"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08852-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,26]]},"assertion":[{"value":"1 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}