本文關(guān)鍵詞： 稀疏表示 字典優(yōu)化 雙峰限制 全局約束 邊緣增強(qiáng)　出處：《太原理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：伴隨著信息化的高速發(fā)展,要求信息處理技術(shù)不斷完善,在大多數(shù)數(shù)字圖像應(yīng)用中,圖像處理和分析通常需要高分辨率圖像或視頻。當(dāng)今,硬件成本已不是問題,但是對于大多數(shù)本就受到污損的低分辨率文本圖像來說,即使硬件設(shè)備足夠清晰,當(dāng)中的文字也無法清晰顯現(xiàn),在這種情況下,文本圖像超分辨率重建技術(shù)尤為重要。國內(nèi)外學(xué)者對超分辨率重建進(jìn)行了許多研究,他們的算法都已成功應(yīng)用于自然圖像,但應(yīng)用于文本圖像時效果不佳。文本圖像是一種獨特的圖像,應(yīng)研究適合它的具體技術(shù)。有些學(xué)者雖提出一些針對文本圖像的算法,但存在兩個問題,一是算法復(fù)雜度高,二是在先驗信息不足的情況下重建效果不好。因此,本文以文本圖像的特征為基礎(chǔ),針對稀疏表示的重建方法進(jìn)行研究,在效率和精度兩方面進(jìn)行改進(jìn),具體研究工作如下:(1)研究文本圖像的退化模型,分析當(dāng)前的幾種圖像重建模型和字典訓(xùn)練算法,對原始稀疏表示的重建算法具體流程進(jìn)行研究和分析。(2)對稀疏表示中的聯(lián)合訓(xùn)練方法進(jìn)行研究和分析,針對聯(lián)合訓(xùn)練算法運算時間長,執(zhí)行效率低的問題,提出一種優(yōu)化的字典訓(xùn)練方法,只需學(xué)習(xí)高分辨率字典,近而由其推導(dǎo)得到低分辨率字典,從而縮短了運算時間,提高了算法的效率。在高分辨率字典學(xué)習(xí)階段,使用K-SVD算法來訓(xùn)練字典;求解稀疏表示系數(shù)階段,通過分析稀疏表示系數(shù)的局部模型,使用高效的特征符號方法進(jìn)行求解。最后進(jìn)行了實驗仿真和分析,對算法的重建效果和執(zhí)行時間都作了對比實驗,運行時間縮短了45.7%,PSNR值和SSIM值稍高于原始的稀疏表示算法,證明算法在保證精度的同時提高了執(zhí)行效率。(3)在優(yōu)化的字典訓(xùn)練方法的基礎(chǔ)上,對文本圖像的特性進(jìn)行研究,針對原始稀疏表示算法重建的圖像不清晰,前景和背景區(qū)分不明顯,不能清晰顯現(xiàn)文字,邊緣不連續(xù)的問題,對全局約束進(jìn)行改進(jìn),引入文本圖像的雙峰限制特性作為正則項來約束重建高分辨率圖像,并使用邊緣增強(qiáng)算法來優(yōu)化增強(qiáng)圖像的邊緣。對算法實驗驗證并與經(jīng)典的稀疏重建方法以及當(dāng)前兩種文本圖像重建的方法進(jìn)行對比和分析。結(jié)果證明本文算法重建的圖像邊緣恢復(fù)得更好,文字和背景區(qū)分更明確。(4)分析碑文圖像的特性,根據(jù)其特性設(shè)計圖像處理流程,首先對它預(yù)處理,并使用本文改進(jìn)的重建方法對預(yù)處理后的碑文進(jìn)行SR重建。最后通過實驗驗證本文方法在碑文圖像恢復(fù)中的可行性和實用性,實驗證明經(jīng)本文超分辨率重建后的圖像,文字邊緣清晰,前景背景區(qū)分明顯,文字容易識別。
[Abstract]:With the rapid development of information technology, information processing technology is constantly improved. In most digital image applications, image processing and analysis usually require high-resolution image or video. Hardware costs are no longer a problem, but for most low-resolution text images that are already tainted, even if the hardware is clear enough, the text in the middle is not clear enough, in this case. Text image super-resolution reconstruction technology is particularly important. Scholars at home and abroad have done a lot of research on super-resolution reconstruction, their algorithms have been successfully applied to natural images. But the application of text image is not good. Text image is a kind of unique image, which should be studied. Some scholars put forward some algorithms for text image, but there are two problems. One is the high complexity of the algorithm, the other is that the reconstruction effect is not good in the case of lack of prior information. Therefore, based on the features of text images, this paper studies the sparse representation reconstruction method. The research work is as follows: 1) the degradation model of text image is studied, and several image reconstruction models and dictionary training algorithms are analyzed. The concrete flow of the original sparse representation reconstruction algorithm is studied and analyzed. (2) the joint training method in sparse representation is studied and analyzed, and the joint training algorithm takes a long time. In this paper, an optimized dictionary training method is proposed, which only needs to learn high-resolution dictionaries and get low-resolution dictionaries from them, thus shortening the operation time. Improve the efficiency of the algorithm. In the learning stage of high-resolution dictionary, K-SVD algorithm is used to train the dictionary; At the stage of solving sparse representation coefficient, the local model of sparse representation coefficient is analyzed, and the efficient characteristic symbol method is used to solve the problem. Finally, the experimental simulation and analysis are carried out. The reconstruction effect and execution time of the algorithm are compared, and the running time is reduced by 45.7% PSNR value and SSIM value slightly higher than the original sparse representation algorithm. It is proved that the algorithm not only ensures the accuracy but also improves the execution efficiency. (3) on the basis of the optimized dictionary training method, the characteristics of the text image are studied, and the image reconstructed by the original sparse representation algorithm is not clear. The distinction between foreground and background is not obvious, the text can not appear clearly, the edge is not continuous, the global constraint is improved, and the bimodal constraint feature of text image is introduced as the regular item to reconstruct high-resolution image. The edge enhancement algorithm is used to optimize the image edge enhancement. The algorithm is verified by experiments and compared with the classical sparse reconstruction method and the current two text image reconstruction methods. The results show that the proposed algorithm is heavy. The built image edges are restored better. The character of the inscription image is analyzed, and the image processing flow is designed according to its characteristics. The first step is to preprocess it. Finally, the feasibility and practicability of the proposed method in the restoration of inscription images are verified by experiments. The experimental results show that the text edge is clear, the foreground background is distinct, and the text is easy to be recognized after super-resolution reconstruction.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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相關(guān)期刊論文 前10條

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本文編號：1454390