使用影像散焦點展延函數信息方法來估測淺景深度圖

Shallow Depth Map Estimation from Image Defocus Blur Point Spread Function Information

詹霖(Hani Ousamah Morad Jamleh)

指導教授 ： 陳中平

國立臺灣大學/電機資訊學院/電子工程學研究所/博士(2014年)

https://doi.org/10.6342/NTU.2014.11140

摘要

The aim of this research is addressing both the influence of the limited aperture size of the optical imaging system of the camera, and the defocus aberration influence on output images in order to measure useful information such as defocus and depth through the MTF (Modulation Transfer Function), further we analyze the existing defocus levels by measuring the size of blur kernels.
One of the goals of our study is to make shallow depth photos with blurry background; photographers need to use cameras such as SLR (single-lens reflex) not only for carefully choosing the best position with respect to the object but also changing the lens effective focal length or aperture size in order to obtain an artistic effect mostly desired in many types of photographs (e.g. portraits), which is not available for normal camera users who prefer to use low cost compact point-and-shot cameras; for their ease of use and convenience.
Nowadays, the size of TFT-LCDs (thin-film-transistor liquid-crystal displays) is getting larger, as a result; it becomes harder to inspect defects that may exist which usually require a human visual examiner to judge the severity of the defects on the final product. These defects; so called mura (Japanese shorthand) are defined as visual blemish with non-uniform shapes and boundaries. It is becoming a very serious unpleasant effect which needs to be detected and inspected in order to characterize the LCD’s quality.
Through this research, we essentially propose two contributions. One that given only two images taken under different camera parameters, we measure a reliable defocus map based on scale-space analysis, then we propagate the defocus measures over edges to the entire image using matting process, eventually we will have a refined dense defocus map, which is utilized in applications such as amplifying the existing blurriness yielding a shallow depth photos from all focused images. On the other hand, it helps extracting the foreground object shape and isolating it from the background. The second contribution is experimentally detecting many types of MURA defects on LCD panels by some low-complex effective post-processing imaging techniques.
Practically; we utilize the computational photography techniques to amplify defocus levels and to detect low contrast defects such as MURA.
Our Computational techniques will allow the average photographers to capture more appealing photos, and the LCD manufacturers to increase their Engineer’s efficiencies and performance. We strongly proof that this study will enable cameras and automated vision systems to embed useful computation with few user interventions.

關鍵字

Defocus Map, Depth of Field, Mura Defect, Computational Photography, Digital Image Processing, Defocus Amplification, Depth From Defocus, Shape From Focus, TFT-LCD, Camera, Computer Vision, Shallow Focus, Defocus Map, Depth Map.

並列摘要

The aim of this research is addressing both the influence of the limited aperture size of the optical imaging system of the camera, and the defocus aberration influence on output images in order to measure useful information such as defocus and depth through the MTF (Modulation Transfer Function), further we analyze the existing defocus levels by measuring the size of blur kernels.
One of the goals of our study is to make shallow depth photos with blurry background; photographers need to use cameras such as SLR (single-lens reflex) not only for carefully choosing the best position with respect to the object but also changing the lens effective focal length or aperture size in order to obtain an artistic effect mostly desired in many types of photographs (e.g. portraits), which is not available for normal camera users who prefer to use low cost compact point-and-shot cameras; for their ease of use and convenience.
Nowadays, the size of TFT-LCDs (thin-film-transistor liquid-crystal displays) is getting larger, as a result; it becomes harder to inspect defects that may exist which usually require a human visual examiner to judge the severity of the defects on the final product. These defects; so called mura (Japanese shorthand) are defined as visual blemish with non-uniform shapes and boundaries. It is becoming a very serious unpleasant effect which needs to be detected and inspected in order to characterize the LCD’s quality.
Through this research, we essentially propose two contributions. One that given only two images taken under different camera parameters, we measure a reliable defocus map based on scale-space analysis, then we propagate the defocus measures over edges to the entire image using matting process, eventually we will have a refined dense defocus map, which is utilized in applications such as amplifying the existing blurriness yielding a shallow depth photos from all focused images. On the other hand, it helps extracting the foreground object shape and isolating it from the background. The second contribution is experimentally detecting many types of MURA defects on LCD panels by some low-complex effective post-processing imaging techniques.
Practically; we utilize the computational photography techniques to amplify defocus levels and to detect low contrast defects such as MURA.
Our Computational techniques will allow the average photographers to capture more appealing photos, and the LCD manufacturers to increase their Engineer’s efficiencies and performance. We strongly proof that this study will enable cameras and automated vision systems to embed useful computation with few user interventions.

並列關鍵字

Defocus Map, Depth of Field, Mura Defect, Computational Photography, Digital Image Processing, Defocus Amplification, Depth From Defocus, Shape From Focus, TFT-LCD, Camera, Computer Vision, Shallow Focus, Defocus Map, Depth Map.

參考文獻

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