2026-04-18T06:58:30Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/217272026-03-13T17:32:51Zcom_2072_452955com_2072_2054col_2072_453069

00925njm 22002777a 4500 dc Hao, Qiaohong author Zhao, Qi author Sbert, Mateu author Feng, Qinghe author Ancuti, Cosmin author Feixas Feixas, Miquel author Vila, Marius author Zhang, Jiawan author info:eu-repo/date/embargoEnd/2022-10-08 2021-10-08 Multi-exposure image fusion has emerged as an increasingly important and interesting research topic in information fusion. It aims at producing an image with high quality by fusing a set of differently exposed images. In this article, we present a pixel-level method for multi-exposure image fusion based on an information-theoretic approach. In our scheme, an information channel between two source images is used to compute the Rényi entropy associated with each pixel in one image with respect to the other image and hence to produce the weight maps for the source images. Since direct weight-averaging of the source images introduce unpleasing artifacts, we employ Laplacian multi-scale fusion. Based on this pyramid scheme, images at every scale are fused by weight maps, and a final fused image is inversely reconstructed. Multi-exposure image fusion with the proposed method is easy to construct and implement and can deliver, in less than a second for a set of three input images of size 512×340, competitive and compelling results versus state-of-art methods through visual comparison and objective evaluation This work is supported by the National Natural Science Foundation of China under grant No.61702359, and by grant PID2019-106426RB-C31 and by grant PID2019-106426RB-C31 from the Agencia Estatal de Investigación (AEI) from Spanish Government. Part of this work was supported by a grant of the Romanian Ministry of Education and Research, CNCS – UEFISCDI, project number PN-III-P1-1.1-TE-2019-1111, within PNCDI III Entropia (Teoria de la informació) Entropy (Information theory) Fusió d'imatges Multisensor data fusion Imatges -- Processament Image processing Information-Theoretic Channel for Multi-exposure Image Fusion