Colonoscopy examinations are widely used for detecting colon cancer and many other colon abnormalities. Unfortunately, the resulting colon videos often have artifacts caused by camera motion and specular highlights caused by light reflections from the wet colon surface. To address these problems, we have developed a method for motion compensated colonoscopy image restoration. Our approach utilizes RANSAC-based image registration to align sequences of N consecutive images in the colonoscopy video and restores each frame of the video using information from these aligned images. We compare image alignment quality when N adjacent images are registered to each other versus registering images with larger step sizes between them. Three types of image pre processing were evaluated in our work. We found that the removal of non-informative images prior to image registration produced better alignment results and reduced processing time. We also evaluated the effects of image smoothing and resizing as a pre processing step for image registration.

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Colonoscopy is a popular procedure which is used to detect an abnormality. Early diagnosis can help to heal many patients. The purpose of this paper is removing/reducing some artifacts to improve the visual quality of colonoscopy videos to provide better information for physicians. This work complements a series of work consisting of three previously published papers. In this paper, optic flow is used for motion compensation, where a number of consecutive images are registered to integrate some information to create a new image that has/reveals more information than the original one. Colon images were classified into informative and noninformative images by using a deep neural network. Then, two different strategies were used to treat informative and noninformative images. Informative images were treated by using Lucas Kanade with an adaptive temporal mean/median filter, whereas noninformative images were treated by using Lucas Kanade with a derivative of Gaussian (LKDOG) and adaptive temporal median images. Comparison showed that this work achieved better results than those achieved by the state-of-the-art strategies for the same degraded colon images data set. The new proposed algorithm reduced the error alignment by a factor of about 0.3, with a 100% successful image alignment ratio. In conclusion, this algorithm achieved better results than the state-of-the-art approaches in case of enhancing the informative images as shown in the results section; also, it helped to reveal some information from noninformative images that have very few details/no details.

Colonoscopy is a procedure that has been used widely to detect the abnormality in a colon. Colonoscopy images suffer from a lot of problems that make it hard for the doctor to investigate/ understand a colon patient. Unfortunately, with the current technology, three is no way for doctors to know if the whole colon surface has been investigated or not. We have developed a method that utilizes RANSAC-based image registration to align sequences of any length in the colonoscopy video and restores each frame of the video using information from these aligned images. We proposed two methods. First method used the deep neural net for the classification of informative and non-informative image. The classification result was used as a preprocessing for alignment method. Also, we proposed a visualization structure for the classification results. The second method used the alignment to decide/classify the bad and good alignment by using two factors. The first factor is the accumulated error and the second factor contain three checking steps that check the pair error alignment beside the geometry transform status. The second method was able to align long sequences.