本文選題：超采樣 + 圖像插值　； 參考：《自動化學報》2016年08期

【摘要】：提出一種以輪廓模板插值和局部自學習相結合的圖像紋理增強超采樣算法,有效地恢復了插值圖像丟失的細節(jié)紋理,抑制了插值圖像邊緣的擴散.該方法通過局部自相似性在原始低分辨圖像中估計高頻信息,對輪廓模板插值圖像的細節(jié)紋理進行了恢復.其中,為了彌補輪廓模板插值缺少先驗知識的缺陷,將原始低分辨率圖像的高頻信息作為先驗知識.為了保證估計的高頻信息最優(yōu),匹配的過程中采用雙匹配,相比較于全局搜索和小窗搜索,提高了效率并保證了匹配精度.此外,使用高斯模糊代替了傳統(tǒng)提取高頻信息的方法,簡化了算法的復雜度,提高了準確性和效率.對估計得到的高頻信息采用高斯函數(shù)加窗,以減小估計出錯和重疊區(qū)的混疊影響.本文算法的訓練庫由原始低分辨圖像自身和插值圖像構成,節(jié)省了生成訓練庫所需的時間和空間.訓練庫的簡化使得高頻信息的估計可以多尺度進行,算法效率得到進一步優(yōu)化.理論分析和實驗結果表明,相比傳統(tǒng)的基于插值、基于自學習的圖像超分辨率方法,本文方法獲得更好的實驗結果,主觀效果得到明顯改善,有效地恢復了圖像的紋理細節(jié),提高了圖像邊緣銳度,避免了產生鋸齒等人工效應,客觀指標得到提高.
[Abstract]:An image texture enhancement oversampling algorithm based on contour template interpolation and local self-learning is proposed, which can effectively restore the lost detail texture of the interpolated image and restrain the edge diffusion of the interpolated image.In this method, the local self-similarity is used to estimate the high-frequency information in the original low-resolution image, and the detailed texture of the contour template interpolation image is restored.In order to make up for the lack of prior knowledge in contour template interpolation, the high frequency information of the original low resolution image is regarded as prior knowledge.In order to ensure the optimal estimation of high frequency information, double matching is used in the matching process. Compared with global search and small window search, the efficiency is improved and the matching accuracy is ensured.In addition, Gao Si fuzziness replaces the traditional method of extracting high frequency information, which simplifies the complexity of the algorithm and improves the accuracy and efficiency.Gao Si function is used to window the estimated high frequency information in order to reduce the aliasing effect of the estimation error and overlapping region.The training library of this algorithm is composed of the original low-resolution image itself and the interpolated image, which saves the time and space needed to generate the training library.With the simplification of the training library, the estimation of high frequency information can be carried out on multiple scales, and the efficiency of the algorithm is further optimized.The theoretical analysis and experimental results show that compared with the traditional image super-resolution method based on interpolation and self-learning, the proposed method achieves better experimental results, and the subjective effect is obviously improved, and the texture details of the image are recovered effectively.The edge sharpness of the image is improved, the manual effect such as sawtooth is avoided, and the objective index is improved.
【作者單位】： 武漢大學電子信息學院;武漢大學測繪遙感信息工程國家重點實驗室;
【基金】：國家自然科學基金(61471272)資助~~
【分類號】：TP391.41

【相似文獻】

相關碩士學位論文 前1條

1 孫澤銳;基于插值圖像的可逆信息隱藏算法研究[D];廣西師范大學;2014年

，

本文編號：1771908