A New family of second-generation wavelets constructed using robust data-prediction lifting scheme
- Almost every application for manipulating and improving photographic images, such as noise reduction and detail enhancement, relies on scale separation.
- Linear translation-invariant (LTI) filtering is the classic and efficient method for achieving scale separation; however the method generates visual artefacts such as halos.
- Edge-preserving smoothing filters avoid these artefacts but are computationally expensive, they are time-consuming and often increase memory requirements.
- The new wavelets accelerate the computation of the edge-preserving smoothing as well as various other popular applications (such as image colorization) at no memory cost.
Family of second-generation wavelets constructed using robust data-prediction lifting schemes. The wavelets make it possible to compute nonlinear data-dependent multi-scale edge-preserving image filtering and processing at computation times which are linear in the number of image pixels.
- Avoids halo artefacts in band-independent multi-scale processing with minimal computational cost and without taking any special precautions.
- Can be used to compute edge-aware interpolation schemes very efficiently, enabling real-time implementation of various applications such as image colorization
- Avoids the difficulties of solving large and poorly conditioned systems of equations
- Significantly accelerates various computational photography applications, achieving multi-scale data-dependent filtering at running times typical of linear translation-invariant (LTI) filtering.
- The software is ready for incorporation into any image processing toolkit or video processing system to provide significantly faster results at reduced cost. It has particular benefits for:
- Edge-preserving smoothing of color images
- Edge-aware noise removal
- Dynamic-range compression
- Scattered data interpolation for image colorization