發(fā)布時(shí)間：2018-05-28 01:30

  本文選題：壓縮感知 + 稀疏表示��； 參考：《湖北工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著圖像信息需求量的與日俱增,在信息采樣與傳輸過(guò)程中,傳統(tǒng)奈奎斯特(Nyquist)定理所帶來(lái)的抽樣資源浪費(fèi)、硬件成本昂貴、信息處理效率低下等局限性問(wèn)題日益突出,而以信號(hào)稀疏性為前提的壓縮感知(Compressed Sensing,CS)采樣編碼技術(shù),就避免了大量冗余數(shù)據(jù)的產(chǎn)生,有效地提高了信號(hào)處理的效率,并且降低了對(duì)設(shè)備采樣速率的要求,極大地節(jié)省了數(shù)據(jù)存儲(chǔ)空間和傳輸成本。本論文結(jié)合CS理論,深入研究基于稀疏表示的圖像重構(gòu)以及去噪方法,將稀疏表示模型用于圖像的傳輸、編碼以及污染圖像的去噪應(yīng)用當(dāng)中,以求高效率、高精準(zhǔn)度地恢復(fù)出完整圖像信號(hào)。論文主要在如下幾個(gè)方面取得了研究成果。(1)針對(duì)圖像混合噪聲去除不足問(wèn)題,提出一種圖像塊分組的加權(quán)編碼方法來(lái)改善圖像去噪質(zhì)量。首先,從訓(xùn)練圖像中利用非局部相似塊來(lái)提取出塊分組;然后,用得到的塊分組來(lái)訓(xùn)練非局部自相似先驗(yàn)?zāi)Ｐ?最后,集成稀疏先驗(yàn)?zāi)Ｐ秃头蔷植孔韵嗨葡闰?yàn)?zāi)Ｐ偷秸齽t化項(xiàng)和編碼框架中。實(shí)驗(yàn)表明,用此方法所得的去噪圖像峰值信噪比較同類方法提高了0.036~2.865dB,獲得了更好的圖像去噪效果。(2)針對(duì)腐化圖像恢復(fù)不足問(wèn)題,給出一種基于PCA的非局部聚類稀疏表示模型來(lái)提高恢復(fù)質(zhì)量。首先,用圖像非局部自相似性來(lái)取得稀疏系數(shù)值;然后,對(duì)觀測(cè)圖像的稀疏編碼系數(shù)進(jìn)行集中聚類;最后,通過(guò)學(xué)習(xí)字典使降噪圖像的稀疏編碼系數(shù)接近原始圖像的編碼系數(shù)。實(shí)驗(yàn)表明,所提方法所得的重建圖像峰值信噪比較同類方法平均提高了0.5653 dB,獲得了更好的圖像重建質(zhì)量。(3)針對(duì)圖像修復(fù)技術(shù)缺陷,設(shè)計(jì)出一種高斯尺度訓(xùn)練稀疏表示方法以達(dá)到高分辨率重建效果。首先,利用非局部相似塊提取出分組的塊群;然后,利用同步稀疏編碼得到非局部擴(kuò)展高斯尺度混合模型;最后,將塊分組模型和高斯尺度稀疏模型聯(lián)合到編碼框架中。實(shí)驗(yàn)表明,該方法既能保留圖像的邊緣又能抑制人工操作造成的不利影響,重建出的圖像峰值信噪比較其他同類競(jìng)爭(zhēng)方法提高了0.02~0.64 dB。
[Abstract]:With the increasing demand of image information, in the process of information sampling and transmission, the traditional Nyquist (Nyquist) theorem is a waste of sampling resources, high cost of hardware and low efficiency of information processing, and the Compressed Sensing (CS) sampling and coding technique based on signal sparsity It avoids the production of a large number of redundant data, effectively improves the efficiency of signal processing, and reduces the requirement for the sampling rate of the equipment, greatly saves the data storage space and transmission cost. This paper studies the image reconstruction and denoising based on the sparse representation based on the CS theory, and uses the sparse representation model to use the sparse representation model. In the application of image transmission, encoding and de-noising of contaminated images in order to efficiently and accurately restore the complete image signal. The main research results are obtained in the following aspects. (1) a weighted coding method for image block grouping is proposed to improve the quality of image denoising in view of the lack of image mixed noise removal. First, the block grouping is extracted from the non local similar blocks in the training image. Then, the non local self similar prior model is trained by the block grouping. Finally, the sparse prior model and the non local self similar prior model are integrated into the regularization term and the coding frame. The experimental results show that the peak signal to noise ratio of the denoised image obtained by this method is shown. Compared with the same method, 0.036~2.865dB improves the image denoising effect. (2) to improve the recovery quality, a non local clustering sparse representation model based on PCA is given to improve the recovery quality. First, the sparse coefficients are obtained by the non local self similarity of the image, and then the sparse coding coefficients of the observed images are obtained. In the end, the sparse coding coefficient of the denoised image is close to the coding coefficient of the original image by learning dictionary. The experiment shows that the peak signal to noise ratio of the reconstructed image is improved by 0.5653 dB, and the quality of the image reconstruction is better. (3) for the defect of image repair technology, a design is designed. The Gauss scale training sparse representation method is used to achieve high resolution reconstruction effect. First, the block groups are extracted from the non local similar blocks; then, the non local extended Gauss scale hybrid model is obtained by the synchronous sparse coding. Finally, the block grouping model and the Gauss ulnar sparse model are combined into the coding framework. The experiment shows that this method is applied to the coding framework. The method not only preserves the edge of the image, but also inhibits the adverse effect caused by manual operation. The reconstructed image peak signal to noise comparison of other similar competition methods improves 0.02~0.64 dB.
【學(xué)位授予單位】：湖北工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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