rekonstruktionsfiltrering

Rekonstruktionsfiltrering, a term originating from German, translates to reconstruction filtering or reconstruction filtering. It refers to a process in signal processing and image processing where a signal or image that has been sampled or discretized is reconstructed into a continuous or higher-resolution form. This is a fundamental concept in fields such as digital signal processing, computer graphics, and medical imaging.

The core idea behind reconstruction filtering is to use interpolation techniques to estimate the values of the signal or image at points that were not originally sampled. When a continuous signal is sampled, information is lost. Reconstruction filtering aims to recover as much of this lost information as possible, or to create a visually smoother or more detailed representation. Common reconstruction filters include nearest neighbor, bilinear interpolation, and bicubic interpolation. The choice of filter depends on the desired trade-off between computational cost, smoothness, and artifact introduction.

In digital signal processing, reconstruction filtering is often applied after digital-to-analog conversion to smooth out the "staircase" effect inherent in discrete signals, ensuring a more accurate representation of the original analog waveform. In image processing, it is used for tasks like resizing images (upscaling or downscaling) and anti-aliasing, where it helps to reduce jagged edges and create a more visually pleasing output. The fidelity of the reconstruction is heavily dependent on the quality of the original sampling and the chosen reconstruction filter.