Decomposition of Multi-Focus Images Using Curvelet Wavelets

Decomposing and fusing multi-focus images using curvelet wavelets is an advanced image processing technique that effectively combines regions from differently focused images to generate a fully focused, clear composite image.

Decomposition Phase The curvelet wavelet is a multiscale, multidirectional transform method capable of capturing curved features and fine details in images. During image decomposition, the curvelet transform breaks down each input image into subbands at different scales and orientations. Compared to traditional wavelet transforms, curvelet transforms are more suitable for processing images with rich edges and textures since they can more effectively represent curve singularities and directional information.

Fusion Strategy After decomposition, subbands from different images need to be fused using specific rules. Local energy maximization is a commonly used fusion strategy with the following core principles: - Calculate local energy within each subband (typically using a sliding window to compute neighborhood pixel energy). - Compare the subband coefficient energies from different input images at the same spatial location, selecting the coefficient with maximum energy as the fused result. This approach effectively preserves high-contrast and sharp details while avoiding information loss or blurring.

Reconstruction of Clear Image The fused subbands are reconstructed into the final fully focused image through an inverse curvelet transform. Due to the efficient representation capabilities of curvelet transforms, the reconstructed image typically retains more details with reduced artifacts.

Application Advantages This method is particularly suitable for multi-focus image fusion applications such as microscopy, medical imaging, or optical devices capturing images with inconsistent focal points. Compared to conventional methods, the fusion technique based on curvelet transforms and local energy maximization significantly improves image quality, making details more clearly distinguishable.