Multi-focus Image Decomposition Using Curvelet Transform

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Resource Overview

Decompose multi-focus images using curvelet wavelets and synthesize them through local energy maximization to produce enhanced clarity images, with implementation involving frequency domain filtering and coefficient selection algorithms.

Detailed Documentation

In this approach, we utilize curvelet transform to decompose multi-focus images into different frequency and orientation components. The decomposition process involves applying curvelet coefficients through digital filtering implementations that capture curved singularities efficiently. Subsequently, we employ a local energy maximization method to synthesize the images, where algorithms compare energy concentrations in curvelet subbands and select coefficients with maximum local energy. This fusion mechanism effectively combines in-focus regions from different source images. Additionally, other image enhancement algorithms such as contrast enhancement or noise reduction techniques can be integrated to further improve image quality. These methodologies collectively produce images with superior detail and sharpness, enabling more precise observation and analysis of image contents through programmable image processing pipelines.

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