<p>Motion blur is a form of image quality degradation, showing as content in the image smearing and not looking sharp. It is usually seen in photography due to relative motion between the camera and the scene (either camera moves or objects in the scene move). It is also seen in human vision systems, primarily on digital displays.</p> <p><br></p> <p>It is often desired to remove motion blurriness from images. Numerous works have been put into reducing motion blur <em>after</em> the image has been formed, e.g., for camera-captured ones. Unlike post-processing methods, we take the approach to prevent/minimize motion blur for both human and camera observation by pre-processing the source image. The pre-processed images are supposed to look sharp upon blurring. Note that, only pre-processing methods can deal with human-observed blurriness since the imagery can't be modified after it is formed on the retina.</p> <p><br></p> <p>Pre-processing methods face more fundamental challenges than post-processing ones. A problem inherent to such methods is the appearance of ringing artifacts which are intensity oscillations reducing the quality of the observed image. We found that these ringing artifacts have a fundamental cause rooted in the blur kernel. The blur kernel usually have very low amplitudes in some frequencies, significantly attenuating the signal intensity in these frequencies when it convolves an image. Pre-processing methods can usually reconstruct the targeted image to the observer but inevitably lose energy in those frequencies, appearing as artifacts. To address the artifact issue, we present a few approaches: (a) aligning the image content and the kernel in the frequency domain, and (b) redistributing their intensity variations elsewhere in the image. We demonstrate the effectiveness of our method in a working prototype, in simulation, and with a user study.</p>

No / Edges are important visual features, providing many cues to the three-dimensional structure of the world. One of these cues is edge blur. Sharp edges tend to be caused by object boundaries, while blurred edges indicate shadows, surface curvature, or defocus due to relative depth. Edge blur also drives accommodation and may be implicated in the correct development of the eye's optical power. Here we use classification image techniques to reveal the mechanisms underlying blur detection in human vision. Observers were shown a sharp and a blurred edge in white noise and had to identify the blurred edge. The resultant smoothed classification image derived from these experiments was similar to a derivative of a Gaussian filter. We also fitted a number of edge detection models (MIRAGE, N(1), and N(3)(+)) and the ideal observer to observer responses, but none performed as well as the classification image. However, observer responses were well fitted by a recently developed optimal edge detector model, coupled with a Bayesian prior on the expected blurs in the stimulus. This model outperformed the classification image when performance was measured by the Akaike Information Criterion. This result strongly suggests that humans use optimal edge detection filters to detect edges and encode their blur.