{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:01:39Z","timestamp":1780531299226,"version":"3.54.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"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":"crossref","award":["U20A20201"],"award-info":[{"award-number":["U20A20201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Program of Hebei","award":["225676163GH"],"award-info":[{"award-number":["225676163GH"]}]},{"name":"Interdisciplinary Foundation of Hebei University of Technology","award":["HEBUT-Y-XKJC- 2021122"],"award-info":[{"award-number":["HEBUT-Y-XKJC- 2021122"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s11760-026-05330-5","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T07:21:20Z","timestamp":1779693680000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GFNET: A blind image quality assessment network based on gabor filter and ResNet50"],"prefix":"10.1007","volume":"20","author":[{"given":"Hao-Ran","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin-Rong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bao-Jun","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,25]]},"reference":[{"key":"5330_CR1","first-page":"1","volume":"74","author":"PK Raghuwanshi","year":"2025","unstructured":"Raghuwanshi, P.K., et al.: An Unsupervised Stereoscopic Image Quality Prediction Model Using Perceptual and Statistical Features of Scene Attributes. IEEE Trans. Instrum. Meas. 74, 1\u201310 (2025)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"12","key":"5330_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2025.3626578","volume":"9","author":"PK Raghuwanshi","year":"2025","unstructured":"Raghuwanshi, P.K., et al.: 3D-CLuDe: A 3-D Image Quality Evaluator Using Correlative Dependencies Between Luminance and Depth Attributes. IEEE Sens. Lett. 9(12), 1\u20134 (2025)","journal-title":"IEEE Sens. Lett."},{"key":"5330_CR3","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep Residual Learning for Image Recognition. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR). Las Vegas, NV, USA, 770-778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5330_CR4","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/s11760-024-03172-7","volume":"18","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., Wang, C., Lv, X., et al.: Attention-driven residual-dense network for no-reference image quality assessment. Signal Image Video Process. 18, 537\u2013551 (2024)","journal-title":"Signal Image Video Process."},{"key":"5330_CR5","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1007\/s11760-022-02335-8","volume":"17","author":"C Liu","year":"2023","unstructured":"Liu, C., Zheng, Y., Liao, K., et al.: No-reference image quality assessment of multi-level residual feature augmentation. Signal Image Video Process. 17, 1275\u20131283 (2023)","journal-title":"Signal Image Video Process."},{"key":"5330_CR6","doi-asserted-by":"publisher","first-page":"70973","DOI":"10.1109\/ACCESS.2024.3402729","volume":"12","author":"T Miyata","year":"2024","unstructured":"Miyata, T.: ZEN-IQA: Zero-Shot Explainable and No-Reference Image Quality Assessment With Vision Language Model. IEEE Access 12, 70973\u201370983 (2024)","journal-title":"IEEE Access"},{"issue":"5","key":"5330_CR7","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/LSP.2010.2043888","volume":"17","author":"AK Moorthy","year":"2010","unstructured":"Moorthy, A.K., Bovik, A.C.: A Two-Step Framework for Constructing Blind Image Quality Indices. IEEE Signal Process. Lett. 17(5), 513\u2013516 (2010)","journal-title":"IEEE Signal Process. Lett."},{"issue":"12","key":"5330_CR8","doi-asserted-by":"publisher","first-page":"3350","DOI":"10.1109\/TIP.2011.2147325","volume":"20","author":"AK Moorthy","year":"2011","unstructured":"Moorthy, A.K., Bovik, A.C.: Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality. IEEE Trans. Image Process. 20(12), 3350\u20133364 (2011)","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"5330_CR9","doi-asserted-by":"publisher","first-page":"3339","DOI":"10.1109\/TIP.2012.2191563","volume":"21","author":"MA Saad","year":"2012","unstructured":"Saad, M.A., Bovik, A.C., Charrier, C.: Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain. IEEE Trans. Image Process. 21(8), 3339\u20133352 (2012)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"5330_CR10","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","volume":"20","author":"A Mittal","year":"2013","unstructured":"Mittal, A., Soundararajan, R., Bovik, A.C.: Making a \u201cCompletely Blind\u2019\u2019 Image Quality Analyzer. IEEE Signal Process. Lett. 20(3), 209\u2013212 (2013)","journal-title":"IEEE Signal Process. Lett."},{"issue":"12","key":"5330_CR11","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","volume":"21","author":"A Mittal","year":"2012","unstructured":"Mittal, A., Moorthy, A.K., Bovik, A.C.: No-Reference Image Quality Assessment in the Spatial Domain. IEEE Trans. Image Process. 21(12), 4695\u20134708 (2012)","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"5330_CR12","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.1109\/TIP.2015.2426416","volume":"24","author":"L Zhang","year":"2015","unstructured":"Zhang, L., Zhang, L., Bovik, A.C.: A Feature-Enriched Completely Blind Image Quality Evaluator. IEEE Trans. Image Process. 24(8), 2579\u20132591 (2015)","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"5330_CR13","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1016\/j.image.2014.06.006","volume":"29","author":"L Liu","year":"2014","unstructured":"Liu, L., et al.: No-reference image quality assessment based on spatial and spectral entropies. Signal Process.-Image Commun. 29(8), 856\u2013863 (2014)","journal-title":"Signal Process.-Image Commun."},{"issue":"4","key":"5330_CR14","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.image.2014.02.004","volume":"29","author":"L Liu","year":"2014","unstructured":"Liu, L., Dong, H., Huang, H., et al.: No-reference image quality assessment in curvelet domain. Signal Process.-Image Commun. 29(4), 494\u2013505 (2014)","journal-title":"Signal Process.-Image Commun."},{"issue":"2","key":"5330_CR15","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1109\/TBC.2018.2816783","volume":"64","author":"X Min","year":"2018","unstructured":"Min, X., et al.: Blind Image Quality Estimation via Distortion Aggravation. IEEE Trans. Broadcast. 64(2), 508\u2013517 (2018)","journal-title":"IEEE Trans. Broadcast."},{"key":"5330_CR16","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhang, Q., Lin, M., et al.: No-reference color image quality assessment: From entropy to perceptual quality. EURASIP J. Image Video Process. 77 (2019)","DOI":"10.1186\/s13640-019-0479-7"},{"key":"5330_CR17","doi-asserted-by":"crossref","unstructured":"Ou, F.-Z., Wang, Y.-G., Zhu, G.: A Novel Blind Image Quality Assessment Method Based on Refined Natural Scene Statistics. IEEE Int. Conf. Image Process. (ICIP) Taipei, Taiwan, 1004-1008 (2019)","DOI":"10.1109\/ICIP.2019.8803047"},{"key":"5330_CR18","doi-asserted-by":"publisher","first-page":"111377","DOI":"10.1109\/ACCESS.2022.3216388","volume":"10","author":"H Wang","year":"2022","unstructured":"Wang, H., Li, S., Ding, J., et al.: SEM Image Quality Assessment Based on Intuitive Morphology and Deep Semantic Features. IEEE Access. 10, 111377\u2013111388 (2022)","journal-title":"IEEE Access."},{"key":"5330_CR19","doi-asserted-by":"crossref","unstructured":"Deng, J.H., et al.: An enhanced image quality assessment by synergizing superpixels and visual saliency. J. Vis. Commun. Image Represent. 88 (2022)","DOI":"10.1016\/j.jvcir.2022.103610"},{"key":"5330_CR20","doi-asserted-by":"publisher","first-page":"6139","DOI":"10.1109\/TIP.2022.3205770","volume":"31","author":"B Chen","year":"2022","unstructured":"Chen, B., Zhu, L., Kong, C., et al.: No-Reference Image Quality Assessment by Hallucinating Pristine Features. IEEE Trans. Image Process. 31, 6139\u20136151 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"5330_CR21","doi-asserted-by":"crossref","unstructured":"Shi, Z.F., Wang, Z., Kong, F.N., et al.: Dual-quality map based no reference image quality assessment using deformable convolution. Digit. Signal Prog. 123 (2022)","DOI":"10.1016\/j.dsp.2022.103398"},{"key":"5330_CR22","doi-asserted-by":"crossref","unstructured":"Domonkos, V.: A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors. Sensors. 22 (2022)","DOI":"10.3390\/s22186775"},{"key":"5330_CR23","doi-asserted-by":"publisher","first-page":"4197","DOI":"10.1109\/TMM.2021.3114551","volume":"24","author":"F-Z Ou","year":"2022","unstructured":"Ou, F.-Z., Wang, Y.-G., Li, J., et al.: A Novel Rank Learning Based No-Reference Image Quality Assessment Method. IEEE Trans. Multimedia 24, 4197\u20134211 (2022)","journal-title":"IEEE Trans. Multimedia"},{"key":"5330_CR24","volume-title":"An End-to-End Perceptual Quality Assessment Method via Score Distribution Prediction","author":"J Liu","year":"2019","unstructured":"Liu, J., Wang, J., Nie, W., et al.: An End-to-End Perceptual Quality Assessment Method via Score Distribution Prediction. Neural Process, Lett (2019)"},{"key":"5330_CR25","doi-asserted-by":"crossref","unstructured":"Ravela, R., Shirvaikar, M., Grecos, C.: No-reference image quality assessment based on residual neural networks (ResNets). Proceedings Volume 11401, Real-Time Image Processing and Deep Learning 114010C (2020)","DOI":"10.1117\/12.2556347"},{"key":"5330_CR26","doi-asserted-by":"crossref","unstructured":"Abayomi-Alli, A., Atinuke, O., Onashoga, S.A., et al.: Facial Image Quality Assessment using an Ensemble of Pre-Trained Deep Learning Models (EFQnet). 20th International Conference on Computational Science and Its Applications, (2020)","DOI":"10.1109\/ICCSA50381.2020.00013"},{"key":"5330_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2021.103296","volume":"80","author":"H Han","year":"2021","unstructured":"Han, H., et al.: Blind image quality assessment with channel attention based deep residual network and extended LargeVis dimensionality reduction. J. Vis. Commun. Image Represent. 80, 103296 (2021)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"5330_CR28","doi-asserted-by":"crossref","unstructured":"Sun, X., Fei, L., Wen, J., et al.: Deep Palmprint Image Quality Assessment Network. 5th Asian Conference on Artificial Intelligence Technology (ACAIT), Haikou, China, (2021)","DOI":"10.1109\/ACAIT53529.2021.9730893"},{"key":"5330_CR29","doi-asserted-by":"crossref","unstructured":"Yan, C., et al.: Precise No-Reference Image Quality Evaluation Based on Distortion Identification. ACM Trans. Multimed. Comput. Commun. Appl. 17 (2021)","DOI":"10.1145\/3468872"},{"key":"5330_CR30","doi-asserted-by":"crossref","unstructured":"Cao, J., et al.: No-reference image quality assessment by using convolutional neural networks via object detection. Int. J. Mach. Learn. Cybern. 13(4), (2022)","DOI":"10.1007\/s13042-022-01611-w"},{"key":"5330_CR31","doi-asserted-by":"crossref","unstructured":"Sendjasni, A., Larabi, M.-C.: Transfer Learning from Vision Transformers or ConvNets for 360-Degree Images Quality Assessment. IEEE Int. Conf. Image Process. (ICIP) Bordeaux, France, 4133-4137 (2022)","DOI":"10.1109\/ICIP46576.2022.9897480"},{"key":"5330_CR32","doi-asserted-by":"crossref","unstructured":"Patil, M.S., Mane, P.B.: Fused Image Classification using Pre-trained Deep Convolutional Neural Networks. Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, 1215-1221 (2023)","DOI":"10.1109\/ICAIS56108.2023.10073743"},{"key":"5330_CR33","doi-asserted-by":"crossref","unstructured":"Han, L., Lv, H., Zhao, Y., et al.: Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment. Sensors. 23(1), (2023)","DOI":"10.3390\/s23010427"},{"key":"5330_CR34","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, C., Jin, S., et al.: PGF-BIQA: Blind image quality assessment via probability multi-grained cascade forest. Comput. Vis. Image Underst. 232 (2023)","DOI":"10.1016\/j.cviu.2023.103695"},{"key":"5330_CR35","doi-asserted-by":"crossref","unstructured":"Qin, G., Hu, R., Liu, Y., et al.: Data efficient image quality assessment with attention-panel decoder. Proc. AAAI Conf. Artif. Intell. 2091-2100 (2023)","DOI":"10.1609\/aaai.v37i2.25302"},{"key":"5330_CR36","doi-asserted-by":"crossref","unstructured":"Saha, A., Mishra, S., Bovik, A.C.: Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Vancouver, BC, Canada, 5846-5855 (2023)","DOI":"10.1109\/CVPR52729.2023.00566"},{"key":"5330_CR37","unstructured":"Fu, H., Wang, Y., Yang, W., et al.: DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild, (2024), arXiv:2405.19996"},{"key":"5330_CR38","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.image.2014.10.009","volume":"30","author":"N Ponomarenko","year":"2015","unstructured":"Ponomarenko, N., et al.: Image database TID2013: Peculiarities, results and perspectives. Signal Process.-Image Commun. 30, 57\u201377 (2015)","journal-title":"Signal Process.-Image Commun."},{"key":"5330_CR39","unstructured":"Sheikh, H.R.: Image and video quality assessment research at live. http:\/\/live.ece.utexas.edu\/research\/quality, (2003)"},{"key":"5330_CR40","doi-asserted-by":"crossref","unstructured":"Lin, H., Hosu, V., Saupe, D.: KADID-10k: A Large-scale Artificially Distorted IQA Database. Eleventh International Conference on Quality of Multimedia Experience (QoMEX), Berlin, Germany, (2019)","DOI":"10.1109\/QoMEX.2019.8743252"},{"key":"5330_CR41","doi-asserted-by":"crossref","unstructured":"Fang, Y., et al.: Perceptual Quality Assessment of Smartphone Photography. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Seattle, WA, USA, 3674-3683 (2020)","DOI":"10.1109\/CVPR42600.2020.00373"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05330-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-026-05330-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05330-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T23:11:11Z","timestamp":1780528271000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-026-05330-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,25]]},"references-count":41,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["5330"],"URL":"https:\/\/doi.org\/10.1007\/s11760-026-05330-5","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,25]]},"assertion":[{"value":"9 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2026","order":4,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"365"}}