{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T11:17:40Z","timestamp":1788434260736,"version":"build-2803163510"},"reference-count":19,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["L252018"],"award-info":[{"award-number":["L252018"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Artifacts, including halos and saturated bright spots, are common in spaceborne optical images and can degrade the reliability of star extraction, space object detection, and photometric analysis. Traditional signal-processing methods, such as morphological filtering and low-rank decomposition, rely on fixed priors that may fail under complex artifact morphologies. Deep restoration networks can improve visual quality but do not explicitly enforce radiometric consistency. Multimodal instruction-driven editing models provide semantic localization capability, but probabilistic diffusion resampling can introduce uncontrolled pixel changes in non-target regions, compromising pixel-level physical consistency. We refer to this problem as editing-induced radiometric drift. To address this problem, we propose PARE, a physics-preserving attention-guided artifact removal framework for spaceborne optical images. Instead of directly using the edited image as the final restoration, PARE treats it as a candidate restoration and derives artifact-region constraints from the image-to-text cross-attention sub-block of the MM-DiT joint attention matrix. These constraints are combined with multi-scale fusion to restrict generative modification to localized artifact regions, thereby enabling artifact suppression while reducing unintended changes in non-target regions. Experiments on simulated and real spaceborne optical image datasets show that PARE achieves effective artifact suppression while improving radiometric preservation. On real on-orbit images, PARE reaches 92.95% artifact mean reduction (AMR) and 98.57% artifact energy reduction (AER), reduces the outer-region mean absolute error (O-MAE) to 0.54, and improves the outer-region structural similarity index (O-SSIM) to 0.997. It also yields the smallest background shifts among all compared methods. These results indicate that PARE provides a favorable trade-off between artifact suppression and radiometric fidelity and offers a practical way to apply generative models to scientific imaging tasks that require pixel-level physical consistency.<\/jats:p>","DOI":"10.3390\/jimaging12090414","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T10:10:33Z","timestamp":1788430233000},"page":"414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Physics-Preserving Attention-Guided Artifact Removal for Spaceborne Optical Images"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7739-7667","authenticated-orcid":false,"given":"Shuxiang","family":"Cai","sequence":"first","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2080-810X","authenticated-orcid":false,"given":"Zuoxun","family":"Hou","sequence":"additional","affiliation":[{"name":"Beijing Institute of Space Mechanics & Electricity, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5315-7661","authenticated-orcid":false,"given":"Haian","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beijing Institute of Space Mechanics & Electricity, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"software","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1464-5203","authenticated-orcid":false,"given":"Zheng","family":"Pan","sequence":"additional","affiliation":[{"name":"Beijing Institute of Space Mechanics & Electricity, China Academy of Space Technology, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0068-8824","authenticated-orcid":false,"given":"Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"ref_1","first-page":"12","article-title":"Cosmic ray rejection algorithm for space observation image","volume":"41","author":"Zhang","year":"2014","journal-title":"Opto-Electron. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"0712003","DOI":"10.3788\/AOS201939.0712003","article-title":"High-accuracy star sensor centroid algorithm based on star image resampling","volume":"39","author":"Cao","year":"2019","journal-title":"Acta Opt. Sin."},{"key":"ref_3","first-page":"318","article-title":"A new method for CCD star image rapid processing","volume":"36","author":"Zhao","year":"2009","journal-title":"Chin. J. Lasers"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhou, D., and Wang, X. (2023). Nonuniform background correction for wide-field surveillance camera. Appl. Sci., 13.","DOI":"10.3390\/app13042594"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wu, C., Wang, K., Wang, T., Du, G., Li, X., and Chen, F. (2026). Structural prior-guided weighted low-rank denoising for short-wave infrared star images. Sensors, 26.","DOI":"10.3390\/s26061980"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018). Image inpainting for irregular holes using partial convolutions. Computer Vision\u2013ECCV 2018, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-01216-8"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Brooks, T., Holynski, A., and Efros, A.A. (2023). InstructPix2Pix: Learning to follow image editing instructions. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52729.2023.01764"},{"key":"ref_9","unstructured":"Black Forest Labs, Batifol, S., Blattmann, A., Boesel, F., Consul, S., Diagne, C., Dockhorn, T., English, J., English, Z., and Esser, P. (2025). FLUX.1 Kontext: Flow matching for in-context image generation and editing in latent space. arXiv."},{"key":"ref_10","unstructured":"Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., and Cohen-Or, D. (2023, January 1\u20135). Prompt-to-Prompt image editing with cross-attention control. Proceedings of the International Conference on Learning Representations (ICLR), Kigali, Rwanda."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kim, C., Shin, H., Hong, E., Yoon, H., Arnab, A., Seo, P.H., Hong, S., and Kim, S. (2025). Seg4Diff: Unveiling open-vocabulary segmentation in text-to-image diffusion transformers. arXiv.","DOI":"10.52202\/085713-2408"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Avrahami, O., Lischinski, D., and Fried, O. (2022). Blended diffusion for text-driven editing of natural images. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.01767"},{"key":"ref_13","unstructured":"Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.-Y., and Ermon, S. (2021, January 3\u20137). SDEdit: Guided image synthesis and editing with stochastic differential equations. Proceedings of the International Conference on Learning Representations (ICLR), Virtual."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tang, L., Jia, M., Wang, Q., Phoo, C.P., and Hariharan, B. (2023, January 10\u201316). Emergent correspondence from image diffusion. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA.","DOI":"10.52202\/075280-0068"},{"key":"ref_15","unstructured":"Liu, S., Han, Y., Xing, P., Yin, F., Wang, R., Cheng, W., Liao, J., Wang, Y., Fu, H., and Han, C. (2025). Step1X-Edit: A practical framework for general image editing. arXiv."},{"key":"ref_16","unstructured":"Bai, J., Bai, S., Yang, S., Wang, S., Tan, S., Wang, P., Lin, J., Zhou, C., and Zhou, J. (2023). Qwen-VL: A versatile vision-language model for understanding, localization, text reading, and beyond. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Peebles, W., and Xie, S. (2023, January 1\u20136). Scalable diffusion models with transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"ref_18","unstructured":"Meituan LongCat Team, Ma, H., Tan, H., Huang, J., Wu, J., He, J.-Y., Gao, L., Xiao, S., Wei, X., and Ma, X. (2025). LongCat-Image Technical Report. arXiv."},{"key":"ref_19","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. 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