本文選題：圖像去模糊 + 交替方向法　； 參考：《華中科技大學(xué)》2016年碩士論文

【摘要】：運(yùn)動(dòng)、散焦或大氣湍流等非理想因素導(dǎo)致成像系統(tǒng)采集到的圖像產(chǎn)生不同程度的模糊,而傳統(tǒng)的目標(biāo)識別方法主要依賴于目標(biāo)自身的特征與表達(dá),模糊的圖像必然將加大目標(biāo)檢測與識別的難度。如何提高模糊圖像的分辨率,為后期信息處理提供有效數(shù)據(jù),具有重要的理論與實(shí)際意義。兩階段圖像去模糊算法可以有效復(fù)原模糊圖像,首先利用自然圖像稀疏性先驗(yàn)估計(jì)模糊核,再利用非盲反卷積求解清晰圖像,采用交替方向法將原本復(fù)雜的優(yōu)化問題轉(zhuǎn)換為若干個(gè)具有封閉解的子問題進(jìn)行迭代求解;為了加快算法的收斂速度,同時(shí)防止求解過程中陷入局部極小值,采用了一種由粗到精的多尺度圖像金字塔求解框架。本文在DSP平臺(tái)(TMS320C6657)上實(shí)現(xiàn)了兩階段去模糊算法。首先分析了DSP的硬件資源、開發(fā)環(huán)境,設(shè)計(jì)了需使用的DSP外設(shè)的驅(qū)動(dòng)軟件。進(jìn)一步,分析算法中主要模塊的處理流程,對關(guān)鍵步驟的實(shí)現(xiàn)進(jìn)行了詳細(xì)的闡述,并通過實(shí)驗(yàn)確定算法參數(shù)的選取規(guī)則。最后,提出了一種基于圖像水平及豎直梯度圖的多節(jié)點(diǎn)并行優(yōu)化方式,并調(diào)用DSPLIB函數(shù)庫對算法中出現(xiàn)的FFT、IFFT、矩陣轉(zhuǎn)置及向量點(diǎn)積等操作進(jìn)行優(yōu)化,同時(shí)利用EDMA3實(shí)現(xiàn)算法中數(shù)據(jù)塊搬移、圖像邊界延拓及矩陣循環(huán)平移等操作。仿真和實(shí)測實(shí)驗(yàn)表明了兩階段圖像去模糊算法在DSP上的實(shí)現(xiàn)對模糊圖像的復(fù)原效果與matlab復(fù)原效果基本相同,而且經(jīng)過對程序一系列的優(yōu)化處理,算法的執(zhí)行速度明顯提高。
[Abstract]:Some non-ideal factors, such as motion, defocusing or atmospheric turbulence, cause the images collected by the imaging system to be blurred to varying degrees, while the traditional methods of target recognition mainly depend on the characteristics and expressions of the target itself.Blurred images will inevitably increase the difficulty of target detection and recognition.How to improve the resolution of blurred images and provide effective data for the later information processing has important theoretical and practical significance.The two-stage image de-blurring algorithm can effectively restore the blurred image. Firstly, the sparse priori estimation of the fuzzy kernel is used in the natural image, and then the non-blind deconvolution is used to solve the clear image.In order to speed up the convergence of the algorithm and avoid falling into a local minimum value, the alternating direction method is used to transform the original complex optimization problem into several subproblems with closed solutions.A multi-scale image pyramid solution framework from coarse to fine is used.In this paper, a two-stage de-blur algorithm is implemented on DSP platform TMS320C6657.Firstly, the hardware resource and development environment of DSP are analyzed, and the driver software of DSP peripheral is designed.Furthermore, the processing flow of the main modules in the algorithm is analyzed, and the key steps are described in detail, and the selection rules of the algorithm parameters are determined by experiments.Finally, a multi-node parallel optimization method based on image horizontal and vertical gradients is proposed, and the DSPLIB function library is called to optimize the operations such as FFTF, matrix transposition and vector dot product.At the same time, EDMA3 is used to realize data block moving, image boundary extension and matrix cyclic translation.The simulation and experimental results show that the effect of the two-stage image de-blurring algorithm on DSP is basically the same as that of the matlab restoration algorithm, and the speed of the algorithm is improved obviously through a series of optimization processing of the program.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前7條

1 李偉鋒;朱金鳳;朱五軍;盧英豪;;基于CCSv4環(huán)境下的單相VSR仿控一體化研究[J];電工文摘;2016年01期

2 吳灝;肖吉陽;范紅旗;付強(qiáng);;TMS320C6678多核DSP的核間通信方法[J];電子技術(shù)應(yīng)用;2012年09期

3 陳曦;汪彥剛;彭思龍;;部分模糊核已知的混合模糊圖像復(fù)原算法[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2010年02期

4 趙剡;李東興;許東;;抑制復(fù)原圖像振鈴波紋的頻域循環(huán)邊界算法[J];北京航空航天大學(xué)學(xué)報(bào);2006年11期

5 李文龍,劉利,湯志忠;軟件流水中的循環(huán)展開優(yōu)化[J];北京航空航天大學(xué)學(xué)報(bào);2004年11期

6 傅玉芳,吳光照,李際軍;基于快速加權(quán)梯度算子的邊緣檢測[J];計(jì)算機(jī)應(yīng)用研究;2004年11期

7 李紅,唐明新,袁愛進(jìn),喬毅;TMS320F2812定點(diǎn)DSP芯片的.cmd文件配置[J];儀器儀表用戶;2004年05期

相關(guān)博士學(xué)位論文 前1條

1 孫科林;基于多核DSP的實(shí)時(shí)圖像處理平臺(tái)研究[D];電子科技大學(xué);2012年

相關(guān)碩士學(xué)位論文 前1條

1 蔣理;毫米波無源探測成像超分辨算法研究及DSP實(shí)現(xiàn)[D];電子科技大學(xué);2009年

，

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