【摘要】：目的基于小波域的多尺度分塊壓縮感知重構(gòu)算法忽略了高頻信號在重構(gòu)過程中的作用,丟失了大量的邊緣與細(xì)節(jié)信息。針對上述問題,提出一種自適應(yīng)多尺度分塊壓縮感知算法,不僅合理利用低頻信息還充分利用圖像的高頻信息,在圖像細(xì)節(jié)復(fù)雜度提高的情況下保證圖像重構(gòu)質(zhì)量的提高。方法首先進行3層小波變換,得到一個低頻信號和9個高頻信號,分別進行小波逆變換后分成大小相同互不重疊的塊,對低頻部分采用2維鄰塊邊緣自適應(yīng)加權(quán)濾波的方法進行處理,對高頻部分采用紋理自適應(yīng)分塊采樣,最后利用平滑投影Landweber(SPL)算法對其進行重構(gòu)。結(jié)果與已有的分塊壓縮感知算法、基于邊緣和方向的分塊壓縮感知算法和基于紋理和方向的分塊壓縮感知算法相比,本文算法在不同的采樣率下,性能均有所提升,代表細(xì)節(jié)信息的高頻信號得到充分重建,改進的算法所得到的重建圖像具有較高的分辨率,尤其對細(xì)節(jié)較為豐富的圖像進行重建后具有較高的峰值信噪比;2維鄰塊邊緣自適應(yīng)加權(quán)濾波有效的去除了重建圖像的塊效應(yīng),且重建時間平均減少了0.3 s。結(jié)論將三層小波變換后的高頻分量作為紋理部分,利用自適應(yīng)多尺度分塊重建出圖像的輪廓與邊緣;將低頻分量直接視為平坦部分,鄰塊邊緣自適應(yīng)加權(quán)濾波重建出圖像細(xì)節(jié),不僅充分利用了圖像的高低頻信息,還減少了平坦塊檢測過程,使得重建時間有效縮短。經(jīng)實驗驗證,本文算法重建圖像質(zhì)量較好,尤其是對復(fù)雜圖像明顯消除了塊效應(yīng),邊緣和紋理細(xì)節(jié)較清晰。因此主要適用于紋理細(xì)節(jié)較復(fù)雜的人臉圖像、建筑圖像和遙感圖像等。
[Abstract]:Aim Multi-scale block compression perceptual reconstruction algorithm based on wavelet domain neglects the function of high frequency signal in the reconstruction process and loses a lot of edge and detail information. To solve the above problems, an adaptive multi-scale block compression sensing algorithm is proposed, which not only makes rational use of the low frequency information but also makes full use of the high frequency information of the image, so as to ensure the improvement of the image reconstruction quality with the increase of the complexity of the image details. Methods three layers of wavelet transform were carried out, one low frequency signal and nine high frequency signals were obtained. After inverse wavelet transform, they were divided into blocks with the same size and no overlap. The low-frequency part is processed by 2-D adjacent block edge adaptive weighted filtering, the high-frequency part is sampled by texture adaptive block sampling, and the smooth projection Landweber (SPL) algorithm is used to reconstruct it. Results compared with the existing block compression sensing algorithm, the edge and direction-based compression sensing algorithm and the texture and direction-based block compression sensing algorithm, the performance of this algorithm is improved at different sampling rates. The high frequency signal representing the detail information is fully reconstructed, and the reconstructed image obtained by the improved algorithm has higher resolution. Especially for the image with rich details, it has a high peak SNR and 2-dimensional edge adaptive weighted filtering to remove the block effect effectively, and the reconstruction time is reduced by 0.3 s on average. Conclusion the high-frequency component of three-layer wavelet transform is taken as texture part, and the contour and edge of image are reconstructed by adaptive multi-scale block, and the low-frequency component is regarded as a flat part directly, and the image details are reconstructed by adaptive weighted filter of adjacent block edge. It not only makes full use of the high and low frequency information of the image, but also reduces the process of flat block detection, which effectively shortens the reconstruction time. Experimental results show that the proposed algorithm has better image quality, especially for complex images, the block effect is eliminated, and the edge and texture details are clear. So it is mainly applied to face image, building image and remote sensing image with complicated texture details.
【作者單位】： 中國礦業(yè)大學(xué)信息與控制工程學(xué)院;
【基金】：國家自然科學(xué)基金項目(U1261105)~~
【分類號】：TN911.73

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