基于非局部相似模型的圖像恢復(fù)算法研究
發(fā)布時(shí)間：2019-01-22 18:27
【摘要】：圖像恢復(fù)旨在盡可能的對原始圖像進(jìn)行高保真度的重建,如何提高圖像的恢復(fù)性能,一直是圖像處理領(lǐng)域的研究熱點(diǎn)。圖像恢復(fù)與圖像采集、存儲和傳輸過程息息相關(guān),有效的圖像信息獲取框架對之后的圖像恢復(fù)過程起著重要作用。壓縮感知,作為一種新興的信號采集理論,給圖像處理領(lǐng)域帶來了革命性突破。該理論能夠以遠(yuǎn)低于香農(nóng)-奈奎斯特采樣定理要求的頻率對稀疏或可壓縮信號進(jìn)行同步采樣和壓縮,并利用少量的隨機(jī)測量值對信號進(jìn)行重建。自提出以來,壓縮感知理論受到了圖像處理領(lǐng)域?qū)W者們的廣泛關(guān)注。圖像恢復(fù),作為壓縮感知理論的核心問題之一,一直是該領(lǐng)域的研究熱點(diǎn)。目前大部分的壓縮感知圖像重建算法都是利用圖像信號在某個(gè)特征空間下的稀疏性構(gòu)建目標(biāo)優(yōu)化函數(shù),但沒有充分考慮圖像信號的其他先驗(yàn)信息,影響了算法的重建性能和算法的適應(yīng)性。對于圖像信號,除了在特定特征空間下的稀疏性以外,還具備很多其他屬性,如圖像的局部特性和結(jié)構(gòu)化屬性等。如何有效利用圖像的這些屬性,進(jìn)一步提高圖像恢復(fù)性能,是本文的研究重點(diǎn)。本文基于非局部相似模型,以壓縮感知中的圖像恢復(fù)算法為對象,進(jìn)行了研究�？紤]圖像的非局部自相似性,提出一種基于圖像相似塊低秩的壓縮感知圖像重建算法,將圖像恢復(fù)問題轉(zhuǎn)化為聚合的相似塊矩陣秩最小問題。算法以最小壓縮感知重建誤差為約束構(gòu)建優(yōu)化模型,并采用加權(quán)核范數(shù)最小化算法求解低秩優(yōu)化問題,很好地挖掘了圖像自身的信息和結(jié)構(gòu)化稀疏特征,保護(hù)了圖像的結(jié)構(gòu)和紋理細(xì)節(jié)。多個(gè)測試圖像,不同采樣率下的實(shí)驗(yàn)證明了算法的有效性,特別是在低采樣率下對于紋理較為豐富的圖像,提出的算法圖像重建質(zhì)量較明顯的優(yōu)于最新的同類算法。進(jìn)一步,考慮在傳統(tǒng)的基于非局部相似模型的圖像恢復(fù)算法中,采用簡單的矩形形狀完成圖像樣本塊的提取和相似塊匹配,破壞了圖像的結(jié)構(gòu)信息,特別是圖像邊緣處的結(jié)構(gòu)特征,提出一種基于非局部相似的形狀自適應(yīng)壓縮感知圖像恢復(fù)算法。算法采用超像素算法進(jìn)行樣本塊提取,并采用與樣本塊相同的形狀進(jìn)行相似塊匹配。由于有效利用了給定圖像的結(jié)構(gòu)信息,所提取到的樣本塊對圖像的邊界依附性更強(qiáng)。同時(shí),由于塊內(nèi)像素冗余度更高,得到的相似塊低秩矩陣的秩更小,對壓縮感知圖像的重建更有益。
[Abstract]:The purpose of image restoration is to reconstruct the original image with high fidelity as much as possible. How to improve the performance of image restoration has always been a hot topic in the field of image processing. Image recovery is closely related to the process of image acquisition, storage and transmission. Effective image information acquisition framework plays an important role in the process of image recovery. Compression sensing, as a new theory of signal acquisition, has brought a revolutionary breakthrough to the field of image processing. The theory can synchronously sample and compress sparse or compressible signals at frequencies far lower than those required by Shannon-Nyquist sampling theorem and reconstruct the signals with a small number of random measurements. Since it was put forward, the theory of compressed perception has been paid more and more attention by many researchers in the field of image processing. Image restoration, as one of the core issues in the theory of compression perception, has been a hot topic in this field. At present, most of the compressed perceptual image reconstruction algorithms use the sparsity of the image signal in a feature space to construct the objective optimization function, but the other prior information of the image signal is not fully considered. The reconstruction performance and adaptability of the algorithm are affected. For image signals, there are many other attributes besides sparsity in specific feature spaces, such as the local and structured properties of images. How to effectively utilize these attributes of images and further improve the performance of image recovery is the focus of this paper. Based on the nonlocal similarity model, the image restoration algorithm in compressed perception is studied in this paper. Considering the nonlocal self-similarity of images, a compressed perceptual image reconstruction algorithm based on the low rank of image similarity blocks is proposed, which transforms the image restoration problem into the rank minimization problem of aggregated similarity block matrix. The algorithm uses minimum compression perceptual reconstruction error as the constraint to construct the optimization model, and uses weighted kernel norm minimization algorithm to solve the low rank optimization problem. Protects the structure and texture details of the image. Experiments on multiple test images at different sampling rates demonstrate the effectiveness of the proposed algorithm, especially for images with rich texture at low sampling rate, and the proposed algorithm is superior to the latest similar algorithms. Furthermore, in the traditional image restoration algorithm based on the non-local similarity model, the simple rectangular shape is used to complete the image sample block extraction and similar block matching, which destroys the structure information of the image. Especially for the structural features of image edges, a shape adaptive compression perceptual image restoration algorithm based on non-local similarity is proposed. The superpixel algorithm is used to extract the sample block and the shape of the sample block is used to match the sample block. Because the structure information of a given image is used effectively, the extracted sample blocks are more dependent on the image boundaries. At the same time, because of the higher pixel redundancy in the block, the rank of the low rank matrix of the similar block is smaller, which is more beneficial to the reconstruction of compressed perceptual image.
【學(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41
本文編號：2413452
[Abstract]:The purpose of image restoration is to reconstruct the original image with high fidelity as much as possible. How to improve the performance of image restoration has always been a hot topic in the field of image processing. Image recovery is closely related to the process of image acquisition, storage and transmission. Effective image information acquisition framework plays an important role in the process of image recovery. Compression sensing, as a new theory of signal acquisition, has brought a revolutionary breakthrough to the field of image processing. The theory can synchronously sample and compress sparse or compressible signals at frequencies far lower than those required by Shannon-Nyquist sampling theorem and reconstruct the signals with a small number of random measurements. Since it was put forward, the theory of compressed perception has been paid more and more attention by many researchers in the field of image processing. Image restoration, as one of the core issues in the theory of compression perception, has been a hot topic in this field. At present, most of the compressed perceptual image reconstruction algorithms use the sparsity of the image signal in a feature space to construct the objective optimization function, but the other prior information of the image signal is not fully considered. The reconstruction performance and adaptability of the algorithm are affected. For image signals, there are many other attributes besides sparsity in specific feature spaces, such as the local and structured properties of images. How to effectively utilize these attributes of images and further improve the performance of image recovery is the focus of this paper. Based on the nonlocal similarity model, the image restoration algorithm in compressed perception is studied in this paper. Considering the nonlocal self-similarity of images, a compressed perceptual image reconstruction algorithm based on the low rank of image similarity blocks is proposed, which transforms the image restoration problem into the rank minimization problem of aggregated similarity block matrix. The algorithm uses minimum compression perceptual reconstruction error as the constraint to construct the optimization model, and uses weighted kernel norm minimization algorithm to solve the low rank optimization problem. Protects the structure and texture details of the image. Experiments on multiple test images at different sampling rates demonstrate the effectiveness of the proposed algorithm, especially for images with rich texture at low sampling rate, and the proposed algorithm is superior to the latest similar algorithms. Furthermore, in the traditional image restoration algorithm based on the non-local similarity model, the simple rectangular shape is used to complete the image sample block extraction and similar block matching, which destroys the structure information of the image. Especially for the structural features of image edges, a shape adaptive compression perceptual image restoration algorithm based on non-local similarity is proposed. The superpixel algorithm is used to extract the sample block and the shape of the sample block is used to match the sample block. Because the structure information of a given image is used effectively, the extracted sample blocks are more dependent on the image boundaries. At the same time, because of the higher pixel redundancy in the block, the rank of the low rank matrix of the similar block is smaller, which is more beneficial to the reconstruction of compressed perceptual image.
【學(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41
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