SLAM-based technology to improve the impact of uneven illumination on minimally invasive surgery.

All traditional surgical procedures cause a lot of harm to the patient, while the biggest advantage of minimally invasive surgery is that the wound is small, the pain is small, and the recovery is fast. Recently, minimally invasive surgery has been widely adopted in the medical field and has been widely used as an important technique. Because of the high degree of coordination between the hand and the eye, there is a high demand for the operating ability of the physician. The images obtained by endoscopy do not have sufficient depth and are difficult to localize. The key to improving the visualization of such surgeries lies in optimizing image processing methods and feature point extraction algorithms that augment SLAM (Simultaneous Localization and Map Construction) techniques. This paper reviews several methods, such as the Contrast Constrained Adaptive Histogram Equalization (CLAHE) method, which solves the illumination problem by optimizing the grayscale transformation. Other methods such as wavelet transform, homomorphic filtering, and multiscale bilaterally weighted retina are also discussed for their image enhancement capabilities. For feature point extraction, AKAZE-ORB and SuperPoint algorithms combined with k-means algorithms are emphasized for their advancement in resolving uneven illumination and ensuring accurate extraction. [ABSTRACT FROM AUTHOR]