Acceleration of Image Processing and Computer Vision Algorithms

Image Processing And Computer Vision

Image processing research has resulted in developing several sophisticated operators that provide visually arresting results. In the recent decade, techniques have been developed that may drastically improve detail in a picture by adopting the style of a master photographer (Aubry et al., 2014), smooth the image for the goal of simplification (Xu et al., 2012; Zhang et al., 2014) and reduce the effects of scattering. Existing operators have a wide range of computing requirements and run times. Certain operators, such as filtering methods, have benefited from almost a decade of focused effort to accelerate their growth. One well-known technique for speeding up a wide variety of image analysis operators is to down sample the picture, run the operator at a low resolution, and then up a sample (J. Chen et al., 2016). This strategy has two significant downsides. The original operators must be assessed on a lower-resolution version of the picture. This may be a considerable disadvantage since certain operators are sluggish, and present implementations are incapable of running at interactive rates, even at different resolutions.