基于多尺度分解的k鄰域隨機(jī)查找快速圖像修復(fù)
doi: 10.11999/JEIT150033
基金項(xiàng)目:
國家自然科學(xué)基金(61300125, 61471160)
Multi-scale Decomposition Based k-nearest-neighbor Random Search for Fast Image Completion
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摘要: 該文提出一種基于多尺度分解的k鄰域隨機(jī)查找快速圖像修復(fù)方法?；陔p邊濾波下采樣分解圖像，從圖像最粗糙層開始，對每一粗糙層采用基于最小堆的k鄰域隨機(jī)查找算法快速搜索最佳匹配塊，利用魯棒優(yōu)先級函數(shù)確定下一待修復(fù)塊。每一粗糙層修復(fù)后用雙邊濾波上采樣重建下一粗糙層，迭代得到最終的修復(fù)結(jié)果。與相關(guān)工作比較，所提方法的修復(fù)結(jié)果能夠保持圖像的細(xì)節(jié)和邊緣信息，取得更高的修復(fù)質(zhì)量。利用客觀指標(biāo)評價修復(fù)結(jié)果。實(shí)驗(yàn)結(jié)果表明該方法有效易行，修復(fù)的圖像具有良好的可視效果。
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關(guān)鍵詞:
- 圖像處理 /
- 圖像修復(fù) /
- 多尺度分解 /
- k鄰域隨機(jī)查找
Abstract: Multi-scale decomposition based k-nearest-neighbor random search for fast image completion is presented. The image is decomposed using the bilateral filtering based down sampling. Starting from the coarsest level image, the most matching patch is searched using k-nearest-neighbor search algorithm based on the minimum heap for each coarse layer. The robust priority function is presented to determine the next patch that should be handled. The lower coarse layer is reconstructed using the bilateral filtering based up sampling after current coarse layer is repaired, so as to get the final result with iterative completion. Compared with related work, the presented algorithm preserves image details and edge information, and obtains higher completion quality. The completion results are evaluated utilizing the objective indictors. The experimental results show that presented method is effective, feasible, and the visual effect of the image completion is pleasing. -
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