本文選題：遙感圖像 + 全參考��； 參考：《西安科技大學(xué)》2017年碩士論文

【摘要】：目前已經(jīng)涌現(xiàn)了大量的圖像質(zhì)量評(píng)價(jià)算法且在評(píng)價(jià)普通圖像時(shí)能夠取得較好的效果。遙感圖像作為一種高空拍攝的高分辨率自然圖像,與普通的自然圖像存在很多區(qū)別,采用現(xiàn)有的圖像質(zhì)量評(píng)價(jià)方法不能達(dá)到要求。因此就需要結(jié)合遙感圖像本身的特點(diǎn)研究出更符合人眼視覺(jué)的質(zhì)量評(píng)價(jià)方法。本文深入分析了結(jié)構(gòu)相似性理論(Structural Similarity,SSIM),針對(duì)遙感圖像邊緣多尺度、紋理錯(cuò)綜復(fù)雜的特點(diǎn),結(jié)合人眼視覺(jué)系統(tǒng),從全參考和無(wú)參考兩個(gè)方面對(duì)SSIM方法進(jìn)行改進(jìn)。人眼對(duì)邊緣區(qū)域及其兩側(cè)的對(duì)比度更加敏感,紋理區(qū)域本身包含整幅圖像的大部分結(jié)構(gòu)信息,然而SSIM將所有像素一視同仁,不符合人眼視覺(jué)系統(tǒng)。因此,本文給出了一種了全參考的基于邊緣紋理區(qū)域質(zhì)量評(píng)價(jià)算法(ET_SSIM)。該方法采用非下采樣輪廓波變換進(jìn)行多尺度分解,將子圖像進(jìn)行k-means邊緣檢測(cè)及細(xì)化融合,最終得到邊緣區(qū)域。對(duì)圖像計(jì)算其梯度幅值,設(shè)置兩個(gè)閾值,將滿足區(qū)間的像素提取出來(lái)得到紋理區(qū)域。然后,使用邊緣計(jì)算對(duì)比度分量,紋理計(jì)算結(jié)構(gòu)相似度分量,對(duì)SSIM進(jìn)行改進(jìn)。由于遙感圖像缺少主觀評(píng)價(jià)數(shù)據(jù)庫(kù),為了更好的檢驗(yàn)本文方法的準(zhǔn)確性,建立了遙感圖像數(shù)據(jù)庫(kù)進(jìn)行驗(yàn)證。實(shí)驗(yàn)表明,本文的全參考方法ET_SSIM比MSE、PSNR、SSIM及GSSIM的線性相關(guān)度分別提高了 22.8%,6.7%,1.3%,0.7%。為了得到一種更加通用的無(wú)參考遙感圖像評(píng)價(jià)方法,本文對(duì)ET_SSIM進(jìn)行改進(jìn)。采用“再降質(zhì)”的方式構(gòu)造出參考圖像,將邊緣紋理區(qū)域與平滑區(qū)域分開評(píng)價(jià),ET_SSIM對(duì)邊緣紋理區(qū)域評(píng)價(jià),SSIM對(duì)平滑區(qū)域評(píng)價(jià),根據(jù)人眼對(duì)邊緣紋理及平滑區(qū)域的不同重視程度加權(quán)求和,得到最終結(jié)果。最后,在相同SSIM值不同主觀感受的遙感圖像集、自建數(shù)據(jù)庫(kù)和LIVE數(shù)據(jù)庫(kù)的實(shí)驗(yàn)說(shuō)明本文方法VSSIM評(píng)價(jià)的單調(diào)性和一致性略低于ET_SSIM,但是作為一種無(wú)參考方法,其準(zhǔn)確性明顯好于SSIM。且本文的無(wú)參考方法VSSIM方法比目前主流的無(wú)參考方法BLIINDS-II線性相關(guān)度提高了 1.3%。
[Abstract]:At present, a large number of image quality evaluation algorithms have emerged and can achieve good results in the evaluation of ordinary images. As a kind of high-resolution natural image, remote sensing image is different from ordinary natural image, and the existing image quality evaluation method can not meet the requirements. Therefore, it is necessary to combine the characteristics of remote sensing images to develop a quality evaluation method that is more suitable for human vision. In this paper, the structural similarity theory is deeply analyzed. Aiming at the multi-scale and intricate texture of remote sensing image, the SSIM method is improved from two aspects: full reference and no reference. The human eye is more sensitive to the contrast of the edge region and its two sides, and the texture region itself contains most of the structural information of the whole image. However, SSIM treats all pixels equally and does not conform to the human visual system. Therefore, an all-reference quality evaluation algorithm based on edge texture region is presented. In this method, the non-downsampling contour wave transform is used for multi-scale decomposition, and the k-means edge detection and thinning fusion are performed on the sub-image, and the edge region is finally obtained. The gradient amplitude of the image is calculated and two thresholds are set to extract the pixels satisfying the range to obtain the texture region. Then, the contrast component is computed by edge and the structural similarity component is calculated by texture, and the SSIM is improved. Because remote sensing image lacks subjective evaluation database, in order to verify the accuracy of this method, a remote sensing image database is established for verification. The experimental results show that the linear correlation of the full reference method ET_SSIM is 22.86.7and 1.30.70% higher than that of MSE PSNRIM and GSSIM, respectively. In order to obtain a more general evaluation method of remote sensing images without reference, this paper improves ET_SSIM. The reference image is constructed by "redegrade" method, and the edge texture area is evaluated separately from the smooth area by ETS IM and the smooth area is evaluated by SSIM. The final results are obtained by weighted summation according to the different emphasis on edge texture and smooth region. Finally, in the remote sensing image set with the same SSIM value and different subjective feelings, the experiments of self-built database and LIVE database show that the monotonicity and consistency of VSSIM evaluation in this paper is slightly lower than that of ETSSS, but as a non-reference method, its accuracy is obviously better than that of SSIM. The linear correlation of the non-reference method VSSIM method is 1.3% higher than that of the current mainstream non-reference method BLIINDS-II method.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP751

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本文編號(hào)：1925914