Fast mosaicing of cystoscopic images from dense correspondence: Combined SURF and TV-L1 optical flow method

In white light cystoscopy, bladder images are characterized by a strong texture and scene illumination variability which complicates image mosaicing. State-of-art methods exhibit high image registration accuracy at the expense of computational time. We propose an algorithm which selects either a feature based method or an optical flow method according to the image texture; for fast and accurate bladder wall mosaicing. Total variation (TV) optical flow method (deduced by duality) guarantees robust registration of poorly textured images. Realistic phantom images are registered with subpixel accuracy with a processing speed-up by a factor of 8 and 16 for two reference methods. Patient data results also illustrate the performance of the algorithm.