{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:10:17Z","timestamp":1760148617199,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"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 for youth scientists of China","doi-asserted-by":"publisher","award":["62203201","LJKZZ20220085","2022-13"],"award-info":[{"award-number":["62203201","LJKZZ20220085","2022-13"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Foundation Research Project of the Educational Department of Liaoning Province","award":["62203201","LJKZZ20220085","2022-13"],"award-info":[{"award-number":["62203201","LJKZZ20220085","2022-13"]}]},{"name":"Cooperation Innovation Plan of Yingkou for Enterprise and Doctor","award":["62203201","LJKZZ20220085","2022-13"],"award-info":[{"award-number":["62203201","LJKZZ20220085","2022-13"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the field of single-image super-resolution reconstruction, GAN can obtain the image texture more in line with the human eye. However, during the reconstruction process, it is easy to generate artifacts, false textures, and large deviations in details between the reconstructed image and the Ground Truth. In order to further improve the visual quality, we study the feature correlation between adjacent layers and propose a differential value dense residual network to solve this problem. We first use the deconvolution layer to enlarge the features, then extract the features through the convolution layer, and finally make a difference between the features before being magnified and the features after being extracted so that the difference can better reflect the areas that need attention. In the process of extracting the differential value, using the dense residual connection method for each layer can make the magnified features more complete, so the differential value obtained is more accurate. Next, the joint loss function is introduced to fuse high-frequency information and low-frequency information, which improves the visual effect of the reconstructed image to a certain extent. The experimental results on Set5, Set14, BSD100, and Urban datasets show that our proposed DVDR-SRGAN model is improved in terms of PSNR, SSIM, and LPIPS compared with the Bicubic, SRGAN, ESRGAN, Beby-GAN, and SPSR models.<\/jats:p>","DOI":"10.3390\/s23104854","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T07:35:50Z","timestamp":1684395350000},"page":"4854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DVDR-SRGAN: Differential Value Dense Residual Super-Resolution Generative Adversarial Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Hang","family":"Qu","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China"}]},{"given":"Huawei","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China"}]},{"given":"Yanlan","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China"}]},{"given":"Jie","family":"Lan","sequence":"additional","affiliation":[{"name":"College of Science, Liaoning University of Technology, Jinzhou 121001, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,18]]},"reference":[{"key":"ref_1","unstructured":"Zhu, S., Zeng, B., and Liu, G. (July, January 29). Image interpolation based on non-local geometric similarities. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Turin, Italy."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1109\/TIP.2015.2499698","article-title":"Multi-Scale Patch-Based Image Restoration","volume":"25","author":"Papyan","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C., and Tang, X. (2016, January 11\u201314). Accelerating the Super-Resolution Convolutional Neural Network. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J., and Lee, K. (2016, January 27\u201330). Deeply-Recursive Convolutional Network for Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.181"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1109\/LSP.2021.3080219","article-title":"Difference Value Network for Image Super-Resolution","volume":"28","author":"Jiang","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., and Kim, H. (2017, January 21\u201326). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image Super-Resolution Using Very Deep Residual Channel Attention Networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hu, X., Mu, H., Zhang, X., Wang, Z., Tan, T., and Sun, J. (2019, January 15\u201320). Meta-SR: A Magnification-Arbitrary Network for Super-Resolution. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00167"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, Z., Yang, J., Liu, Z., Yang, X., and Wu, W. (2019, January 15\u201320). Feedback Network for Image Super-Resolution. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00399"},{"key":"ref_11","unstructured":"Salimans, T., Goodfellow, I., and Zaremba, W. (2016, January 5\u201310). Improved techniques for training gans. Proceedings of the Annual Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_12","unstructured":"Huang, H., Yu, S., and Wang, C. (2018). An introduction to image synthesis with generative adversarial nets. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative Adversarial Networks: An Overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., and Husz\u00e1r, F. (2017, January 21\u201326). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bulat, A., Yang, J., and Tzimiropoulos, G. (2018, January 8\u201314). To learn image super-resolution, use a GAN to learn how to do image degradation first. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01231-1_12"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., and Wu, S. (2018, January 8\u201314). Esrgan: Enhanced Super-Resolution Generative Adversarial Networks. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, X., Chen, Q., and Ng, R. (2019, January 15\u201320). Zoom to Learn, Learn to Zoom. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00388"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lugmayr, A., Danelljan, M., and Gool, V. (2020, January 23\u201328). Srflow: Learning the super-resolution space with normalizing flow. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58558-7_42"},{"key":"ref_20","unstructured":"Rad, M.S., Bozorgtabar, B., Marti, U.V., Basler, M., Ekenel, H.K., and Thiran, J.P. (November, January 27). SROBB: Targeted Perceptual Loss for Single Image 299 Super-Resolution. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, W., Zhou, K., Qi, L., Lu, L., Jiang, N., Lu, J., and Jia, J. (March, January 22). Best-Buddy GANs for Highly Detailed Image Super-Resolution. Proceedings of the 36th AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.","DOI":"10.1609\/aaai.v36i2.20030"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ma, C., Rao, Y., and Cheng, Y. (2020, January 13\u201319). Structure-Preserving Super Resolution with Gradient Guidance. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00779"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., and Kong, Y. (2018, January 18\u201323). Residual Dense Network for Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zeiler, D., and Fergus, R. (2014, January 6\u201312). Visualizing and Understanding Convolutional Networks. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Agustsson, E., and Timofte, R. (2017, January 21\u201326). Ntire 2017 Challenge on Single Image Super-Resolution: Dataset and Study. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., and Guillemot, C. (2012, January 25\u201330). Neighbor embedding based single-image super-resolution using semi-nonnegative matrix factorization. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan.","DOI":"10.1109\/ICASSP.2012.6288125"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Liu, S., and Zhang, J. (2018, January 18\u201323). Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00113"},{"key":"ref_28","unstructured":"Martin, D., Fowlkes, C., and Tal, D. (2001, January 7\u201314). A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. Proceedings of the Eighth IEEE International Conference on Computer Vision, Vancouver, BC, Canada."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huang, B., Singh, A., and Ahuja, N. (2015, January 7\u201312). Single Image Super-Resolution from Transformed Self-Exemplars. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., and Efros, A. (2018, January 18\u201323). The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4854\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:37:20Z","timestamp":1760125040000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4854"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,18]]},"references-count":31,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104854"],"URL":"https:\/\/doi.org\/10.3390\/s23104854","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,5,18]]}}}