Abstract
Image fusion involves combining useful details from input images into a single image and the image can convey the complete particulars. Image fusion finds wide application in remote sensing, change detection, and medical imaging. The presence of noise in the input images limits the accuracy of fusion. To overcome this limitation, a hybrid filtering technique using gradient and guided filter is proposed to fuse satellite data. Source images are denoised using a hybrid filtering framework comprising of a gradient filter followed by an edge-preserving guided filter. The denoised images are fused using the traditional discrete wavelet transform. The results are compared against the fused outputs for traditional filters like median filter, Wiener filter, and guided filter by computing performance metrics such as entropy, Peak Signal-to-Noise Ratio(PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), gradient-based quality index (QAB/F), and CPU time. The results show that the hybrid filtering based fusion outperforms other filtering-based fusion techniques.
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Asokan, A., Anitha, J. (2020). Satellite Image Enhancement Using Hybrid Denoising Method for Fusion Application. In: Das, H., Pattnaik, P., Rautaray, S., Li, KC. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 1119. Springer, Singapore. https://doi.org/10.1007/978-981-15-2414-1_12
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DOI: https://doi.org/10.1007/978-981-15-2414-1_12
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