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Edge enhancement is an image processing filter that enhances the edge contrast of an image or video in an attempt to improve its acutance (apparent sharpness).. The filter works by identifying sharp edge boundaries in the image, such as the edge between a subject and a background of a contrasting color, and increasing the image contrast in the area immediately around the edge.
Manual anti aliasing, artistic technique done in pixel art graphics to smooth transitions between shapes, to soften lines or to blur edges. Computer Generated Imagery (CGI), specific-technology or application of computer graphics for creating or improving images in art, printed media, simulators, videos and video games. [1]
The resulting image is larger than the original, and preserves all the original detail, but has (possibly undesirable) jaggedness. The diagonal lines of the "W", for example, now show the "stairway" shape characteristic of nearest-neighbor interpolation. Other scaling methods below are better at preserving smooth contours in the image.
Gaussian blurring is commonly used when reducing the size of an image. When downsampling an image, it is common to apply a low-pass filter to the image prior to resampling. This is to ensure that spurious high-frequency information does not appear in the downsampled image . Gaussian blurs have nice properties, such as having no sharp edges, and ...
Median filtering is one kind of smoothing technique, as is linear Gaussian filtering. All smoothing techniques are effective at removing noise in smooth patches or smooth regions of a signal, but adversely affect edges. Often though, at the same time as reducing the noise in a signal, it is important to preserve the edges.
Mipmapping is a standard technique used to save some of the filtering work needed during texture minification. [2] It is also highly beneficial for cache coherency - without it the memory access pattern during sampling from distant textures will exhibit extremely poor locality, adversely affecting performance even if no filtering is performed.
As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems (early vision [1]), such as image smoothing, the stereo correspondence problem, image segmentation, object co-segmentation, and many other computer vision problems that can be formulated in terms of energy minimization.
Video post-processing is the process of changing the perceived quality of a video on playback (done after the decoding process). Image scaling routines such as linear interpolation, bilinear interpolation, or cubic interpolation can for example be performed when increasing the size of images; this involves either subsampling (reducing or shrinking an image) or zooming (enlarging an image).
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