Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal

In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.Comment: This is a final version accepted by CVPR 201

Joint Blind Motion Deblurring and Depth Estimation of Light Field

Removing camera motion blur from a single light field is a challenging task since it is highly ill-posed inverse problem. The problem becomes even worse when blur kernel varies spatially due to scene depth variation and high-order camera motion. In this paper, we propose a novel algorithm to estimate all blur model variables jointly, including latent sub-aperture image, camera motion, and scene depth from the blurred 4D light field. Exploiting multi-view nature of a light field relieves the inverse property of the optimization by utilizing strong depth cues and multi-view blur observation. The proposed joint estimation achieves high quality light field deblurring and depth estimation simultaneously under arbitrary 6-DOF camera motion and unconstrained scene depth. Intensive experiment on real and synthetic blurred light field confirms that the proposed algorithm outperforms the state-of-the-art light field deblurring and depth estimation methods

RENDERING STOCHASTIC & ACCUMULATION BUFFER UNTUK EFEK MOTION BLUR PADA ENGINE OGRE 3D

Sebuah foto dari obyek yang bergerak dengan cepat akan menghasilkan efek motion blur. Sebaliknya, seluruh hasil proses render komputer grafis akan menghasilkan gambar yang tajam. Untuk menghasilkan hasil proses render yang realis, dibutuhkan efek motion blur. Banyak pendekatan dilakukan untuk menghasilkan efek motion blur, antara lain accumulation buffer, post- process motion blur, dan metode stochastic. Dalam jurnal ini, kami mengembangkan serta membandingkan motion blur pada engine OGRE 3D. Metode yang digunakan adalah accumulation buffer dan metode stochastic. Dibandingkan dengan metode accumulation buffer, metode stochastic dapat mengurangi artifak bergaris yang dihasilkan metode accumulation buffer. Namun metode stochastic dapat menghasilkan noise acak. Kata Kunci: Motion blur, stochastic rendering, accumulation butter, OGRE 3D engine