Nima Khademi Kalantari

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High dynamic range (HDR) imaging from a set of sequential exposures is an easy way to capture high-quality images of static scenes, but suffers from artifacts for scenes with significant motion. In this paper, we propose a new approach to HDR reconstruction that draws information from all the exposures but is more robust to camera/scene motion than previous(More)
The most successful approaches for filtering Monte Carlo noise use feature-based filters (e.g., cross-bilateral and cross non-local means filters) that exploit additional scene features such as world positions and shading normals. However, their main challenge is finding the optimal weights for each feature in the filter to reduce noise but preserve scene(More)
In this paper, a novel arrangement for quantizer levels in the Quantization Index Modulation (QIM) method is proposed. Due to perceptual advantages of logarithmic quantization, and in order to solve the problems of a previous logarithmic quantization-based method, we used the compression function of mu-Law standard for quantization. In this regard, the host(More)
Despite significant progress in high dynamic range (HDR) imaging over the years, it is still difficult to capture high-quality HDR video with a conventional, off-the-shelf camera. The most practical way to do this is to capture alternating exposures for every LDR frame and then use an alignment method based on optical flow to register the exposures(More)
In this paper a Voice Activity Detection approach is proposed which applies a voting algorithm to decide on the existence of speech in audio signal. For this purpose, the proposed approach uses three different short time features along with the pattern of spectral peaks of every frame. Spectral peaks pattern is appropriate for determining vowel sounds in(More)
Patch-based synthesis is a powerful framework for numerous image and video editing applications such as hole-filling, retargeting, and reshuffling. In all these applications , a patch-based objective function is optimized through a patch search-and-vote process. However, existing techniques typically use fixed-size square patches when comparing the distance(More)
Dart-throwing can generate ideal Poisson-disk distributions with excellent blue noise properties, but is very computationally expensive if a maximal point set is desired. In this paper, we observe that the Poisson-disk sampling problem can be posed in terms of importance sampling by representing the available space to be sampled as a probability density(More)