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This paper presents a computationally efficient algorithm for smoothly space-variant Gaussian blurring of images. The proposed algorithm uses a specialized filter bank with optimal filters computed through principal component analysis. This filter bank approximates perfect space-variant Gaussian blurring to arbitrarily high accuracy and at greatly reduced(More)
We present a novel selective blurring algorithm that mimics the optical distance blur effects that occur naturally in cameras and eyes. The proposed algorithm provides a realistic simulation of distance blurring, with the desirable properties of aiming to mimic occlusion effects as occur in natural blurring, and of being able to handle any number of(More)
We introduce the concept of depth-based blurring to achieve an aesthetically acceptable distortion when reducing the bitrate in image coding. The proposed depth-based blurring is a prefiltering that reduces high-frequency components by mimicking the limited depth of field effect that occurs in cameras. To cope with the challenge of avoiding intensity(More)
We present a method for computing a function of average multi-viewer eye sensitivity based on the Geisler & Perry contrast threshold formula, and, from this, the cut-off frequency map (as used in foveation filtering) that is optimal in the sense of discarding frequencies in least-noticeable-first order. Existing approaches usually solve the(More)
We present a novel algorithm for enhancing an image or video frame with depth of field. The algorithm deals with occlu-sive blurring effects as occur in real cameras and can handle a variation in blur which is continuous up to blur level quantization granularity, with asymptotic complexity O(N log<sup>2</sup> N) in terms of time and memory for an N-pixel(More)
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