Processing with Contrast-Based Pixel Selection on Wavelet Decomposition Foundation
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Resource Overview
This method involves partitioning the image into blocks and performing wavelet decomposition to obtain low-frequency and high-frequency components. It calculates the contrast for each block to classify them into clear blocks and blurred blocks, with adjacent areas between clear and blurred blocks defined as boundary regions. During fusion, clear blocks are directly selected as the corresponding fused blocks. For boundary regions, a contrast-based pixel selection method is applied on the wavelet decomposition basis, implementing weighted fusion algorithms to optimize edge transitions and enhance image clarity.
Detailed Documentation
In this approach, we first divide the image into blocks and perform wavelet decomposition to extract low-frequency and high-frequency components. Subsequently, we calculate the contrast for each image block and classify them into clear blocks and blurred blocks. The adjacent regions between clear and blurred blocks are designated as boundary areas. During the fusion process, clear blocks are directly selected as the corresponding fused blocks. For boundary regions, we implement a contrast-based pixel selection method applied to the wavelet-decomposed components, utilizing algorithms like weighted averaging or maximum contrast selection at the pixel level. This processing strategy enhances image clarity and quality by effectively handling transitions between distinct regions.
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