1 / 13

Computational Photography Final project – D eblur

Computational Photography Final project – D eblur. 601415004 巫承熹. Background. In photography, it have different type of blur Camera shake( 相機晃動 ) User moving hands Scene motion( 場景位移 ) Objects in the scene moving Defocus blur ( 失焦 ) Depth of field effect.

karlyn
Télécharger la présentation

Computational Photography Final project – D eblur

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Computational Photography Final project – Deblur 601415004 巫承熹

  2. Background In photography, it have different type of blur • Camera shake(相機晃動) • User moving hands • Scene motion(場景位移) • Objects in the scene moving • Defocus blur (失焦) • Depth of field effect Prof. Shing-Min Liu, Computational Photography: Applied Graphics and Imaging course lecture(2013)

  3. Introduction 在拍照攝影時，有上述幾種造成影像模糊的原因。其中，相機的晃動，可以藉由裝設感應器(如：加速度計、陀螺儀)來擷取移動向量，以解決模糊。這類的去模糊方法已經廣泛的被應用在相機的光學防手震(OIS)上。 HTC官方網站, http://www.htc.com/tw/zoe/stabilization/

  4. Introduction 然而，場景(物件)的位移卻無法直接由相機上的感應器直接的量測，因此，在這裡我們試著用基本原理，去除場景(物件)的簡單位移造成的模糊。

  5. Estimation the Degradation Function Give the blur source image: We try to recover the image using estimation the degradation funtion g(x,y) = f(x,y) * h(x,y) (1) G(u,v) =F(u,v) * H(u,v) (2)

  6. Method Step1: Load the blur source image , then convert it to the frequency domain using FFT. Get . Step2: Calculate , in frequency domain. In this case, we only have horizontal motion, therefore, , which , and a is parameter between 0.11 to 0.15. Step3: for avoiding case of “division zero”, we set a threshold , which is a small positive constant. If , then let , otherwise, . Step4: Use inverse FFT to convert from frequency domain to spatial domain .

  7. Flow Cart Load Image File FFT Inverse FFT Display

  8. User Interface ( Qt ) QT是一個跨平台的C++應用程式開發框架，被廣泛的應用於開發GUI程式。選擇使用這個GUI的介面，除了他擁有影像處理能力，還有因為它的支援跨平台的特性。這使我們可以將程式移植至嵌入式平台上執行，以提升系統攜帶的方便性與實用性。

  9. User Interface Load Image file (.bmp only) Source Image Display here Result Image Display here Set the parameter Click the button to display reult

  10. Result 因為每個來源影像的blur kernel 是未知的，因此根據不同的來源影像，會有不同的最佳輸入參數。 a=0.14 a=0.11

  11. Result 然而，並不是每個case都可以成功地找到參數，使圖片去模糊。在這個case中，圖片經過轉換產生的許多雜訊。但雖然如此，但是車子的下半部分仍然成功地去模糊。 DemoVideo( Youtube,Demo.avi) a=0.156

  12. 結論 在照相越來流行的時代，Deblur是非常實用的領域，目前市面上利用OIS解決相機晃動的作法已是非常普及，然而拍攝物體的移動是未知的，因此，我們運用預測DegradationFunction的方式來去除模糊。 目前我們以手動調整參數來實現。未來，可以利用一些設定參數的策略，來完善這個研究。

  13. Reference [1] Prof. Shing-Min Liu, Computational Photography: Applied Graphics and Imaging course lecture(2013)  [2] HTC官方網站, http://www.htc.com/tw/zoe/stabilization/ [3]Wikipedia, http://zh.wikipedia.org/zh-hant/Qt [4]M. Ben-Ezra and S. K. Nayar  "Motion-based motion deblurring",  IEEE Trans. Pattern Anal. Mach. Intell.,  vol. 26,  no. 6,  pp.689 -698 2004 

More Related