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Imaging plays a key role in many diverse areas, such as astronomy, remote sensing, microscopy or tomography, just to name few. Due to imperfections of measuring devices (optical degradations, limited size of sensors, camera shake) and instability of observed scene (object motion, air turbulence), captured images are blurred, noisy and of insufficient spatial or temporal resolution. Image restoration methods try to improve their quality.
For principle reasons, these methods need to know the type of degradation process and make less or more restrictive assumptions about the scene or the image we want to get. Intuitively, the more restrictions we are able to prescribe the better results we can achieve. Very general ones are for example simple smoothing constraints assuming that the image contains large homogenous areas. More restrictive are those allowing only a certain type of blurring (out-of-focus or motion blur in a certain direction) or for example the assumption that the whole scene is planar.
Another possibility which makes the problem easier is to consider more than one image of the same scene (multiframe imaging). In this case, we need much less additional knowledge about the scene or degradation process.
Topics
To simplify the problem, the blur is usually assumed to be homogenous in the whole image. Because the blur can be modeled by convolution in this case, the reverse problem to find the sharp image is called deconvolution. If a mathematical description of the blur is not available, which is the case in most real situations, we refer to the problem as blind deconvolution. One way to overcome the instability typical for deconvolution of a single image is to use multiple images capturing the same scene but blurred in a different way, so called multichannel blind deconvolution.
You can read more about the topic of multichannel blind deconvolution and an algorithm of this type we developed or directly download our Matlab implementation here (MBD application).
Super-resolution is the process of combining a sequence of low resolution images in order to produce a higher resolution image or sequence.
Read more about super-resolution or download Matlab implementation of a super-resolution algorithm we developed here (BSR application).
The blur caused by camera motion is a serious problem in many areas of optical imaging such as remote sensing, aerial reconnaissance or digital photography. As a rule, this problem occurs when low ambient light conditions prevent an imaging system from using sufficiently short exposure times, resulting in a blurred image due to the relative motion between a scene and the imaging system. For example, the cameras attached to airplanes and helicopters are blurred by the forward motion of the aircraft and vibrations. Similarly when taking photographs by hand under dim lighting conditions, camera shake leads to objectionable blur.
Read more about this topic and about an algorithm we developed for deblurring of images degraded by a special type of camera motion.
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| Project details: | |
| Duration: | started 2001 |
| Contact person: | Filip Šroubek |
| Involved people: | Jan Flusser, Filip Šroubek, Michal Šorel, Barbara Zitová |
