Panigrahy Chinmaya, Seal Ayan, Kumar Mahato Nihar, Krejcar Ondrej, Herrera-Viedma Enrique
Appl Opt. 2020 Jul 1;59(19):5642-5655. doi: 10.1364/AO.391234.
Multi-focus image fusion is defined as "the combination of a group of partially focused images of a same scene with the objective of producing a fully focused image." Normally, transform-domain-based image fusion methods preserve the textures and edges in the blend image, but many are translation variant. The translation-invariant transforms produce the same size approximation and detail images, which are more convenient to devise the fusion rules. In this work, a translation-invariant multi-focus image fusion approach using the à-trous wavelet transform is introduced, which uses fractal dimension as a clarity measure for the approximation coefficients and Otsu's threshold to fuse the detail coefficients. The subjective assessment of the proposed method is carried out using the fusion results of nine state-of-the-art methods. On the other hand, eight fusion quality metrics are considered for the objective assessment. The results of subjective and objective assessment on grayscale and color multi-focus image pairs illustrate that the proposed method is competitive and even better than some of the existing methods.
多聚焦图像融合被定义为“将同一场景的一组部分聚焦图像进行组合，以生成一幅全聚焦图像”。通常，基于变换域的图像融合方法能在融合图像中保留纹理和边缘，但许多方法具有平移变异性。平移不变变换会产生相同尺寸的近似图像和细节图像，这对于设计融合规则更为方便。在这项工作中，引入了一种使用à- trous小波变换的平移不变多聚焦图像融合方法，该方法使用分形维数作为近似系数的清晰度度量，并使用大津阈值来融合细节系数。使用九种最先进方法的融合结果对所提方法进行主观评估。另一方面，客观评估考虑了八种融合质量指标。对灰度和彩色多聚焦图像对的主观和客观评估结果表明，所提方法具有竞争力，甚至优于一些现有方法。
