Single image focus editing

A challenging problem in image restoration is to recover an image with a blurry foreground. Such images can easily occur with modern cameras, when the auto-focus aims mistakenly at the background (which will appear sharp) instead of the foreground, where usually the object of interest is. In this paper we propose an automatic procedure that (i) estimates the amount of out-of-focus blur, (ii) segments the image into foreground and background incorporating clues from the blurriness, (iii) recovers the sharp foreground, and finally (iv) blurs the background to refocus the scene. On several real photographs with blurry foreground and sharp background, we demonstrate the effectiveness and limitations of our method.

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“…First, we compare in Fig. 4 our approach against a recent method by Zhang and Cham [5]. Close inspection, e.g.…”

Section: Resultsmentioning

confidence: 99%

“…[5] estimates a depth map by analysing edge profiles, and is able to refocus. Their method requires enough sharp step edges in the images.…”

Section: Related Workmentioning

confidence: 99%

Automatic foreground-background refocusing

Loktyushin

Harmeling

2011

2011 18th IEEE International Conference on Image Processing

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“…Similarly for depth from diffusion [10], [11], [12], [13] authors have estimated blur parameter for defocus and created the depth map using diffusion, assuming scene as one sided so that the problem of focal plane ambiguity does not exist.…”

Section: Introductionmentioning

confidence: 99%

Resolving focal plane ambiguity in depth map creation from defocus blur using chromatic aberration

Kumar

Gupta

Venkatesh

2015

2015 10th International Conference on Information, Communications and Signal Processing (ICICS)

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Focal plane ambiguity in depth map creation from defocus blur has remained an challenging problem. In this paper, we present a method to resolve this issue with the help of Chromatic Aberration(CA). CA is a distortion referred to focal length variation of the lens with wavelength of light. When light, a mixture of various monochromatic components, passes through a lens, multiple focal planes are generated due to CA. There also exists an inherent ordering in the defocus blur of the object for different RGB components depending on whether the object lies in the near or far focus region. The ordering reverses as soon as focal planes are crossed. By using this difference in ordering of the defocus amount for different RGB components of an object, we can deduce whether the object is present in front of or behind the image plane, hence using this information, a more reliable depth map can be obtained.

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“…A N edge mapping technique has a wide range of applications, including auto-focusing imaging system [1] and 3D imaging [2]. Conventional edge mapping techniques extract the edge information at software level as in Sobel, Prewitt, and Robert methods.…”

Section: Introductionmentioning

confidence: 99%

A Low-Power Edge Detection Image Sensor Based on Parallel Digital Pulse Computation

Lee

Chao

Lee

et al. 2015

IEEE Trans. Circuits Syst. II

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An all digital low-power CMOS edge detection image sensors array is presented. Each pixel contains a voltagecontrolled ring oscillator to achieve low power and cost efficient digital only edge detection. While conventional edge detection methods require high computing power as well as large chip area to process intensity maps, this work implements all-digital parallel processing algorithm that detects differences between neighboring pixel pairs on-chip, hence reducing the aforementioned power and cost overheads. In particular, a simple columnshared frequency comparator enables low power operation by eliminating arithmetic computations with large memory requirement. Such simple edge detection algorithm allows the processor area to be less than 16% of the entire image sensor, therefore maximizing the proportion of active optical area. The prototype image sensor presented in this work is fabricated using a fourmetal 180 nm CMOS image sensor process and contains 105×92 pixels. An individual pixel size is 8×8µm 2 with fill-factor of 11.69%, while the total chip area is 1×1.3 mm 2 . The image sensor exhibits a frame rate of 30 frame/sec and a power consumption of 8 mW which is 27.7 nW/pixel/frame at VDD of 1.6 V.Sobel (0.08) Prewitt (0.08) Our Original 1549-7747 (c)

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Single image focus editing

Cited by 23 publications

References 30 publications

Automatic foreground-background refocusing

Automatic foreground-background refocusing

Resolving focal plane ambiguity in depth map creation from defocus blur using chromatic aberration

A Low-Power Edge Detection Image Sensor Based on Parallel Digital Pulse Computation

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