{"title":"Image fusion research based on the Haar-like multi-scale analysis","authors":"","doi":"10.1186/s13634-024-01118-2","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>In view of the serious color and definition distortion in the process of the traditional image fusion, this study proposes a Haar-like multi-scale analysis model, in which Haar wavelet has been modified and used for the medical image fusion to obtain even better results. Firstly, when the improved Haar wavelet basis function is translated, inner product and down-sampled with each band of the original image, the band is decomposed into four sub-images containing one low-frequency subdomain and three high-frequency subdomains. Secondly, the different fusion rules are applied in the low-frequency domain and the high-frequency domains to get the low-frequency sub-image and the high-frequency sub-images in each band. The four new sub-frequency domains are inverse-decomposed to reconstruct each new band. The study configures and synthesizes these new bands to produce a fusion image. Lastly, the two groups of the medical images are used for experimental simulation. The Experimental results are analyzed and compared with those of other fusion methods. It can be found the fusion method proposed in the study obtain the superior effects in the spatial definition and the color depth feature, especially in color criteria such as OP, SpD, CR and SSIM, comparing with the other methods.</p>","PeriodicalId":11816,"journal":{"name":"EURASIP Journal on Advances in Signal Processing","volume":"21 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EURASIP Journal on Advances in Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s13634-024-01118-2","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
In view of the serious color and definition distortion in the process of the traditional image fusion, this study proposes a Haar-like multi-scale analysis model, in which Haar wavelet has been modified and used for the medical image fusion to obtain even better results. Firstly, when the improved Haar wavelet basis function is translated, inner product and down-sampled with each band of the original image, the band is decomposed into four sub-images containing one low-frequency subdomain and three high-frequency subdomains. Secondly, the different fusion rules are applied in the low-frequency domain and the high-frequency domains to get the low-frequency sub-image and the high-frequency sub-images in each band. The four new sub-frequency domains are inverse-decomposed to reconstruct each new band. The study configures and synthesizes these new bands to produce a fusion image. Lastly, the two groups of the medical images are used for experimental simulation. The Experimental results are analyzed and compared with those of other fusion methods. It can be found the fusion method proposed in the study obtain the superior effects in the spatial definition and the color depth feature, especially in color criteria such as OP, SpD, CR and SSIM, comparing with the other methods.
摘要 针对传统图像融合过程中色彩和清晰度失真严重的问题，本研究提出了一种类 Haar 多尺度分析模型，其中对 Haar 小波进行了改进，并将其用于医学图像融合，以获得更好的效果。首先，将改进后的 Haar 小波基函数与原始图像的每个频带进行平移、内积和下采样，将频带分解为包含一个低频子域和三个高频子域的四个子图像。其次，在低频域和高频域应用不同的融合规则，得到每个波段的低频子图像和高频子图像。对四个新的子频域进行反分解，重建每个新频段。该研究对这些新波段进行配置和合成，以生成融合图像。最后，两组医学图像被用于实验模拟。对实验结果进行了分析，并与其他融合方法进行了比较。可以发现，与其他方法相比，本研究提出的融合方法在空间定义和色彩深度特征方面，尤其是在 OP、SpD、CR 和 SSIM 等色彩标准方面，都取得了卓越的效果。
期刊介绍：
The aim of the EURASIP Journal on Advances in Signal Processing is to highlight the theoretical and practical aspects of signal processing in new and emerging technologies. The journal is directed as much at the practicing engineer as at the academic researcher. Authors of articles with novel contributions to the theory and/or practice of signal processing are welcome to submit their articles for consideration.
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