{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:51:34Z","timestamp":1777704694295,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,6,21]]},"abstract":"<jats:p>Infrared (IR) and visible (VIS) image fusion technology combines the complementary information of the same scene from IR and VIS imaging sensors to generate a composite image, which is beneficial to post image-processing tasks. In order to achieve good fusion performance, a method by combining rolling guidance filter (RGF) and convolutional sparse representation (CSR) is proposed. In the proposed method, RGF is performed on every pre-registered IR and VIS source images to obtain their detail layers and base layer. Then, the detail layers are fused with a serious of weighted coefficients produced by joint bilateral filer (JBF). The base layer is decomposed into a sub-detail-layer and a sub-base-layer. CSR is applied to fuse the sub-detail-layer and averaging strategy is used to fuse the sub-base-layer. Finally, the fused image is reconstructed by adding the fused detail layer and base layer. Experimental results demonstrate the superiority of our proposed method both in subjective and objective assessment.<\/jats:p>","DOI":"10.3233\/jifs-201494","type":"journal-article","created":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T12:08:55Z","timestamp":1620994135000},"page":"10603-10616","source":"Crossref","is-referenced-by-count":6,"title":["Infrared and visible image fusion via rolling guidance filter and convolutional sparse representation"],"prefix":"10.1177","volume":"40","author":[{"given":"Feiqiang","family":"Liu","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China"},{"name":"School of Aeronautics & Astronautics, Sichuan University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihui","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, 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