基于局部方向能量的魯棒圖像融合算法
Local Orientation Energy Based Robust Image Fusion Algorithm
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摘要: 針對(duì)現(xiàn)有小波類圖像融合算法在特征表達(dá)上的不足，將對(duì)偶樹復(fù)數(shù)小波變換引入圖像融合中。Robins等的研究表明，局部能量對(duì)各類圖像特征的表達(dá)和定位具有穩(wěn)健性?；趯?duì)偶樹復(fù)數(shù)小波變換，定義了局部方向能量和局部能量，結(jié)合人類視覺系統(tǒng)對(duì)圖像特征的響應(yīng)特性，定義了局部帶限對(duì)比度，表達(dá)特征的顯著性。實(shí)時(shí)圖像融合系統(tǒng)中，輸入可能被隨機(jī)噪聲污染。根據(jù)圖像特征和噪聲局部方向能量分布不同的特點(diǎn)，定義了局部方向能量熵，用以自適應(yīng)改善帶限對(duì)比度，提高融合過程對(duì)噪聲的魯棒性。對(duì)融合算法仿真結(jié)果的主客觀性能分析，充分驗(yàn)證了本文提出的魯棒的圖像融合算法的卓越性能。Abstract: The dual-tree complex wavelet transform is employed in image fusion, to round the deficiencies of existing wavelet based algorithms in characteristics representation. The study of Robins et al.. shows that the local energy is robust in the representing and locating of all kinds of image features. Based on dual-tree complex wavelet transform, the local orientation energy and local energy are defined, which in conjunction with the response characteristic of human visual system to image features, the local banded contrast is finally formulated. In the real time image fusion system, inputs may be corrupted with random noises. The local orientation energy entropy is formulated, according to the different orientation energy distribution of features and noises, to modulate the local banded contrast adaptively. As a result, the robustness to noise is improved. The performance evaluations of the proposed algorithm both subjectively and objectively manifest the excellent efficacy of the new scheme.
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