{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T03:06:30Z","timestamp":1777950390622,"version":"3.51.4"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:00:00Z","timestamp":1777852800000},"content-version":"vor","delay-in-days":123,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>The dynamic characteristics of event cameras demonstrate unique advantages in extreme environments; however, their inherent spatiotemporal asynchrony and noise sensitivity instead become serious drawbacks during extreme image reconstruction of coal mine substations. To overcome the problems of nonconstant noise coupling and existing static convolutional networks in dealing with vibration\u2010induced motion artifacts, an event\u2010based spatiotemporal disentangled aggregated image reconstruction (ESDAR) method is proposed here. ESDAR is a method to enhance FireNet. The proposed method integrates three innovations. First, it introduces an asymptotic denoising approach inspired by the diffusion model, which effectively decouples high\u2010frequency noise and low\u2010frequency textures. Second, it employs a deformable convolutional mechanism to achieve spatiotemporal alignment between event streams and fuzzy grayscale images. Third, the recurrent unit is substituted with a time convolution network enhanced with deformable convolution (DF\u2010TCN), thereby achieving a significant reduction in the number of parameters, with a decrease of 48.32%. The effectiveness of this method was evaluated via 1000 synthetic sequences and 1670 real frames (captured at a coal mine substation). The findings of this study demonstrate that ESDAR is markedly superior to FireNet with respect to performance. The results revealed a 39.5% reduction in the mean square error (MSE), a 4.2\u2009dB increase in the peak signal\u2010to\u2010noise ratio (PSNR), a 26% reduction in the local perceptual image similarity metric (LPIPS), and a 3.39% increase in the structural similarity index (SSIM). This method effectively achieves a balance between noise suppression and detail preservation under hardware constraints, providing a feasible solution to address image reconstruction in high\u2010noise, vibration\u2010intensive industrial environments.<\/jats:p>","DOI":"10.1155\/int\/4139129","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T07:06:02Z","timestamp":1777878362000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Event\u2010Based Spatiotemporal Disentangled Aggregation for Image Reconstruction"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2560-2150","authenticated-orcid":false,"given":"Yanwei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5118-6130","authenticated-orcid":false,"given":"Chubin","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6134-3290","authenticated-orcid":false,"given":"Zhenhui","family":"Min","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4650-7106","authenticated-orcid":false,"given":"Qingju","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,5,4]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.psep.2021.01.046"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2020.3008413"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3096985"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmm.2023.3290432"},{"key":"e_1_2_10_5_2","doi-asserted-by":"crossref","unstructured":"WengW. 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