【摘要】：隨著多媒體技術(shù)的不斷發(fā)展,人們對于圖像質(zhì)量的要求越來越高。超分辨率技術(shù)在固定傳感器精度的情況下實(shí)現(xiàn)了圖像質(zhì)量的提升,逐漸成為研究熱點(diǎn)。近年來,基于信號稀疏表示的壓縮感知理論在圖像去噪,雷達(dá)成像等圖像處理領(lǐng)域均取得了顯著進(jìn)展,利用壓縮感知理論解決圖像超分辨率重構(gòu)問題,突破了傳統(tǒng)超分辨率方法基于先驗(yàn)信息約束的固有局限,具有極高的研究價(jià)值。本文以壓縮感知及其在超分辨率重構(gòu)中的應(yīng)用為研究主線,對單幀圖像超分辨率重建,多幀圖像超分辨率重建中的關(guān)鍵問題展開研究。針對重構(gòu)圖像邊緣模糊問題,本文提出了基于壓縮感知的單幀圖像超分辨率重構(gòu)框架,采用訓(xùn)練的過完備字典代替?zhèn)鹘y(tǒng)小波基,提高了圖像稀疏表示性能。同時(shí)引入了過完備字典與觀測矩陣聯(lián)合訓(xùn)練的迭代優(yōu)化方法,進(jìn)一步降低觀測矩陣與稀疏基之間的相關(guān)性,較好地恢復(fù)了圖像細(xì)節(jié)信息。在多幀超分辨率重建中,本文首先分析圖像退化理論模型,闡述了低分辨率圖像序列之間具有的高相似性及信息冗余,在此基礎(chǔ)上,本文提出將分布式壓縮感知理論中的聯(lián)合稀疏模型應(yīng)用到多幀圖像超分辨率重構(gòu)中,實(shí)驗(yàn)表明,采用本文方法在減少數(shù)據(jù)量的情況下保證了圖像的重構(gòu)質(zhì)量。
[Abstract]:With the continuous development of multimedia technology, people demand more and more high image quality. Super-resolution technology has achieved the improvement of image quality under the condition of fixed sensor precision, and has gradually become a research hotspot. In recent years, the theory of compressed sensing based on sparse signal representation has made remarkable progress in image processing such as image denoising, radar imaging and so on. The problem of image super-resolution reconstruction is solved by using the theory of compressed sensing. It breaks through the inherent limitation of the traditional super-resolution method based on the prior information constraint and has high research value. In this paper, compression perception and its application in super-resolution reconstruction are the main research thread, and the key problems in single-frame image super-resolution reconstruction and multi-frame image super-resolution reconstruction are studied. In order to solve the edge blur problem of reconstructed image, this paper proposes a single frame image super-resolution reconstruction framework based on compression perception. The trained over-complete dictionary is used to replace the traditional wavelet basis to improve the performance of image sparse representation. At the same time, an iterative optimization method of joint training of overcomplete dictionaries and observation matrices is introduced to further reduce the correlation between observation matrices and sparse bases and to restore the image details. In the multi-frame super-resolution reconstruction, the theoretical model of image degradation is first analyzed, and the high similarity and information redundancy among low-resolution image sequences are expounded. In this paper, the joint sparse model of distributed compression perception theory is applied to the super-resolution reconstruction of multi-frame images. The experiments show that the proposed method can guarantee the quality of image reconstruction under the condition of reducing the amount of data.
【學(xué)位授予單位】：北京郵電大學(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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本文編號：2255915