Blur parameters identification for simultaneous defocus and motion blur
نویسندگان
چکیده
منابع مشابه
Clip Space Sample Culling for Motion Blur and Defocus Blur
Motion blur and defocus blur are two common visual effects for rendering realistic camera images. This paper presents a novel clip space culling for stochastic rasterization to render motion and defocus blur effects. Our proposed algorithm reduces the sample coverage using the clip space information in camera lens domain (UV) and time domain (T). First, samples outside the camera lens were cull...
متن کاملImproved Dual-Space Bounds for Simultaneous Motion and Defocus Blur
Our previous paper on stochastic rasterization [Laine et al. 2011] presented a method for constructing time and lens bounds to accelerate stochastic rasterization by skipping the costly 5D coverage test. Although the method works for the combined case of simultaneous motion and defocus blur, its efficiency drops when significant amounts of both effects are present. In this paper, we describe a ...
متن کاملLayered Reconstruction for Defocus and Motion Blur
Light field reconstruction algorithms can substantially decrease the noise in stochastically rendered images. Recent algorithms for defocus blur alone are both fast and accurate. However, motion blur is a considerably more complex type of camera effect, and as a consequence, current algorithms are either slow or too imprecise to use in high quality rendering. We extend previous work on real-tim...
متن کاملRecovering Affine Motion and Defocus Blur Simultaneously
Motion in depth and/or zooming cause defocus blur. We show how the defocus blur in an image can be recovered simultaneously with affine motion. We introduce the theory, develop a solution method and demonstrate the validity of the theory and the solution by conducting experiments with real scenery.
متن کاملPractical Layered Reconstruction for Defocus and Motion Blur
We present several practical improvements to a recent layered reconstruction algorithm for defocus and motion blur. We leverage hardware texture filters, layer merging and sparse statistics to reduce computational complexity. Furthermore, we restructure the algorithm for better load-balancing on graphics processors, albeit at increased memory usage. We show performance gains of 2− 5× with an al...
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ژورنال
عنوان ژورنال: CSI Transactions on ICT
سال: 2014
ISSN: 2277-9078,2277-9086
DOI: 10.1007/s40012-014-0039-3