Algorithms for Rendering Depth of Field Effects for Synthetic Image Generation and Computational Photography (original) (raw)
Related papers
Algorithms for rendering depth of field effects in computer graphics
Computer generated images by default render the entire scene in perfect focus. Both camera optics and the human visual system have limited depth of field, due to the finite aperture or pupil of the optical system. For more realistic computer graphics as well as to enable artistic control over what is and what is not in focus, it is desirable to add depth of field blurring. Starting with the work of Potmesil and Chakravarty[33][34], there have been numerous approaches to adding depth of field effects to computer graphics. Published work in depth of field for computer graphics has been previously surveyed by Barsky [2][3]. Later, interactive depth of field techniques were surveyed by Demers [12]. Subsequent to these surveys, however, there have been important developments. This paper surveys depth of field approaches in computer graphics, from its introduction to the current state of the art. Figure 1: (left) Image before and after depth of field has been added via postprocessing (cou...
Software simulation of depth of field effects in video from small aperture cameras
2010
This thesis proposes a technique for post processing digital video to introduce a simulated depth of field effect. Because the technique is implemented in software, it affords the user greater control over the parameters of the effect (such as the amount of defocus, aperture shape, and defocus plane) and allows the effect to be used even on hardware which would not typically allow for depth of field.
Journal of Electronic Imaging, 2014
The depth of field (DoF) effect is a useful tool in photography and cinematography because of its aesthetic value. However, capturing and displaying dynamic DoF effect were until recently a quality unique to expensive and bulky movie cameras. A computational approach to generate realistic DoF effects for mobile devices such as tablets is proposed. We first calibrate the rear-facing stereo cameras and rectify the stereo image pairs through FCam API, then generate a low-res disparity map using graph cuts stereo matching and subsequently upsample it via joint bilateral upsampling. Next, we generate a synthetic light field by warping the raw color image to nearby viewpoints, according to the corresponding values in the upsampled high-resolution disparity map. Finally, we render dynamic DoF effect on the tablet screen with light field rendering. The user can easily capture and generate desired DoF effects with arbitrary aperture sizes or focal depths using the tablet only, with no additional hardware or software required. The system has been examined in a variety of environments with satisfactory results, according to the subjective evaluation tests. 1 Introduction Dynamic depth of field (DoF) effect is a useful tool in photography and cinematography because of its aesthetic value. Capturing and displaying dynamic DoF effect were until recently a quality unique to expensive and bulky movie cameras. Problems such as radial distortion may also arise if the lens system is not setup properly. Recent advances in computational photography enable the user to refocus an image at any desired depth after it has been taken. The hand-held plenoptic camera 1 places a micro-lens array behind the main lens, so that each microlens image captures the scene from a slightly different viewpoint. By fusing these images together, one can generate photographs focusing at different depths. However, due to the spatial-angular tradeoff 2 of the light field camera, the resolution of the final rendered image is greatly reduced. To overcome this problem, Georgiev and Lumsdaine 3 introduced the focused plenoptic camera and significantly increased spatial resolution near the main lens focal plane. However, angular resolution is reduced and may introduce aliasing effects to the rendered image. Despite recent advances in computational light field imaging , the costs of plenoptic cameras are still high due to the complicated lens structures. Also, this complicated structure makes it difficult and expensive to integrate light field cameras into small hand-held devices like smartphones or tablets. Moreover, the huge amount of data generated by the plenop-tic camera prohibits it from performing light field rendering on video streams. To address this problem, we develop a light field rendering algorithm on mobile platforms. Because our algorithm works on regular stereo camera systems, it can be directly
Stereo Vision based Depth of Field Rendering on a Mobile Device
Electronic Imaging, 2014
The Depth of Field (DoF) effect is a useful tool in photography and cinematography because of its aesthetic values. However, capturing and displaying dynamic DoF effect was until recently a quality unique to expensive and bulky movie cameras. In this paper, we propose a computational approach to generate realistic DoF effects for mobile devices such as tablets. We first calibrate the rear-facing stereo cameras and rectify stereo image pairs through FCam API, then generate a low-res disparity map using graph cuts stereo matching and subsequently upsample it via joint bilateral upsampling. Next we generate a synthetic light field by warping the raw color image to nearby viewpoints according to corresponding values in the upsampled high resolution disparity map. Finally, we render dynamic DoF effect on the tablet screen with light field rendering. The user can easily capture and generate desired DoF effects with arbitrary aperture sizes or focal depths using the tablet only with no additional hardware or software required. The system has been tested in a variety of environments with satisfactory results.