多曝光圖像融合中的運動檢測與鬼影去除方法研究
發(fā)布時間：2018-08-01 10:01
【摘要】：伴隨著數(shù)碼攝像技術的不斷發(fā)展,數(shù)字圖像越來越多地出現(xiàn)在人們的生產(chǎn)、生活和科學研究當中。近年來,高動態(tài)范圍成像技術的研究和進展,極大地促進了數(shù)字成像技術朝著高清晰度和高信息量方向的發(fā)展。同時,這一技術也被越來越多地應用到數(shù)字攝像、遙測遙感、安防監(jiān)控等領域。本文在闡述了數(shù)字成像、高動態(tài)范圍成像、多曝光圖像融合、鬼影消除等技術原理的基礎上,總結了現(xiàn)有的多曝光圖像融合算法,并基于操作域與參考圖像對算法進行了分類和比較。在此基礎上提出了兩種新的多曝光圖像融合中的運動檢測與鬼影消除算法。本文的研究內容和主要工作如下:1、深入研究了高動態(tài)范圍成像技術,尤其是多曝光圖像融合技術。闡述了動態(tài)場景下運動檢測與鬼影去除技術的理論基礎、國內外研究現(xiàn)狀及當前的技術難點,并針對現(xiàn)有的運動檢測與鬼影去除算法進行了分類和比較;2、首次提出了一種高效的基于類內一致性與類間一致性的"鬼影"檢測和去除算法,以實現(xiàn)高動態(tài)范圍(HDR)圖像的無鬼影重建。通過對選定的參考圖像和圖像序列中的其他圖像進行直方圖映射匹配運算,代替以往直接對圖像數(shù)據(jù)操作來檢測運動的算法。這種方法可以使場景中的大部分細節(jié)得以保留下來,尤其是圖像中過曝光或欠曝光的區(qū)域,并大大降低了后期運動檢測的難度。此外,考慮到不同圖像同一位置的像素間的內部一致性與同一幅圖像里相鄰像素間的外部一致性,將運動檢測模型建立在超像素層面。該方法有效地對輸入的低動態(tài)范圍圖像序列進行了校準,同時在去除鬼影的前提下最大限度地保留了圖像中的細節(jié);3、首次提出一種基于結構一致性與對比度質量的運動檢測與鬼影去除算法。通過比較其他圖像與參考圖像之間的結構一致性大小來對運動區(qū)域進行檢測。為了將場景中的細節(jié)更多的保留下來,引入了對比度指標來進行可見性評價,并在對比度地圖的指導下,將中間圖像序列進行無縫融合,生成一幅無鬼影的圖像,同時大部分場景中的細節(jié)得以保留。此外,該方法大大降低了傳統(tǒng)算法的運算量。
[Abstract]:With the development of digital camera technology, more and more digital images appear in people's production, life and scientific research. In recent years, the research and development of high dynamic range imaging technology has greatly promoted the development of digital imaging technology towards high definition and high information content. At the same time, this technology is more and more used in digital camera, remote sensing, security monitoring and other fields. Based on the introduction of the principles of digital imaging, high dynamic range imaging, multi-exposure image fusion and ghost elimination, this paper summarizes the existing multi-exposure image fusion algorithms. The algorithm is classified and compared based on the operation field and reference image. On this basis, two new motion detection and ghost cancellation algorithms in multi-exposure image fusion are proposed. The research contents and main work of this paper are as follows: 1. The high dynamic range imaging technology, especially the multi-exposure image fusion technology, is studied in depth. This paper expounds the theoretical basis of motion detection and ghost removal technology in dynamic scene, the current research situation and the current technical difficulties at home and abroad. Based on the classification and comparison of the existing motion detection and ghost removal algorithms, an efficient "ghost shadow" detection and removal algorithm based on intra-class and inter-class consistency is proposed for the first time. In order to achieve a high dynamic range of (HDR) images without ghost reconstruction. By using histogram mapping matching operation on the selected reference image and other images in the image sequence, the motion detection algorithm is replaced by the previous direct operation of the image data. This method can keep most of the details in the scene, especially in the over-exposed or under-exposed areas of the image, and greatly reduce the difficulty of post-motion detection. In addition, considering the internal consistency between pixels in the same location of different images and the external consistency between adjacent pixels in the same image, the motion detection model is established at the super-pixel level. This method can effectively calibrate the input image sequences with low dynamic range. At the same time, under the premise of removing the ghost image, the details of the image are kept to the maximum extent. A motion detection and ghost removal algorithm based on structure consistency and contrast quality is proposed for the first time. The motion region is detected by comparing the structure consistency between other images and reference images. In order to keep more details in the scene, the contrast index is introduced to evaluate the visibility, and under the guidance of contrast map, the intermediate image sequence is seamlessly fused to produce a non-ghost image. At the same time, the details of most of the scenes are preserved. In addition, this method greatly reduces the computational complexity of the traditional algorithm.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
本文編號：2157214
[Abstract]:With the development of digital camera technology, more and more digital images appear in people's production, life and scientific research. In recent years, the research and development of high dynamic range imaging technology has greatly promoted the development of digital imaging technology towards high definition and high information content. At the same time, this technology is more and more used in digital camera, remote sensing, security monitoring and other fields. Based on the introduction of the principles of digital imaging, high dynamic range imaging, multi-exposure image fusion and ghost elimination, this paper summarizes the existing multi-exposure image fusion algorithms. The algorithm is classified and compared based on the operation field and reference image. On this basis, two new motion detection and ghost cancellation algorithms in multi-exposure image fusion are proposed. The research contents and main work of this paper are as follows: 1. The high dynamic range imaging technology, especially the multi-exposure image fusion technology, is studied in depth. This paper expounds the theoretical basis of motion detection and ghost removal technology in dynamic scene, the current research situation and the current technical difficulties at home and abroad. Based on the classification and comparison of the existing motion detection and ghost removal algorithms, an efficient "ghost shadow" detection and removal algorithm based on intra-class and inter-class consistency is proposed for the first time. In order to achieve a high dynamic range of (HDR) images without ghost reconstruction. By using histogram mapping matching operation on the selected reference image and other images in the image sequence, the motion detection algorithm is replaced by the previous direct operation of the image data. This method can keep most of the details in the scene, especially in the over-exposed or under-exposed areas of the image, and greatly reduce the difficulty of post-motion detection. In addition, considering the internal consistency between pixels in the same location of different images and the external consistency between adjacent pixels in the same image, the motion detection model is established at the super-pixel level. This method can effectively calibrate the input image sequences with low dynamic range. At the same time, under the premise of removing the ghost image, the details of the image are kept to the maximum extent. A motion detection and ghost removal algorithm based on structure consistency and contrast quality is proposed for the first time. The motion region is detected by comparing the structure consistency between other images and reference images. In order to keep more details in the scene, the contrast index is introduced to evaluate the visibility, and under the guidance of contrast map, the intermediate image sequence is seamlessly fused to produce a non-ghost image. At the same time, the details of most of the scenes are preserved. In addition, this method greatly reduces the computational complexity of the traditional algorithm.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
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