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.
Median filter is adopted to match the noise statistics of the degradation seeking good quality smoothing images. Two methods are suggested in this paper(Pentagonal-Hexagonal mask and Scan Window Mask), the study involved modified median filter for improving noise suppression, the modification is considered toward more reliable results. Modification median filter (Pentagonal-Hexagonal mask) was found gave better results (qualitatively and quantitatively ) than classical median filters and another suggested method (Scan Window Mask), but this will be on the account of the time required. But sometimes when the noise is line type the cross 3x3 filter preferred to another one Pentagonal-Hexagonal with few variation. Scan Window Mask gave bett
... Show MoreShot boundary detection is the process of segmenting a video into basic units known as shots by discovering transition frames between shots. Researches have been conducted to accurately detect the shot boundaries. However, the acceleration of the shot detection process with higher accuracy needs improvement. A new method was introduced in this paper to find out the boundaries of abrupt shots in the video with high accuracy and lower computational cost. The proposed method consists of two stages. First, projection features were used to distinguish non boundary transitions and candidate transitions that may contain abrupt boundary. Only candidate transitions were conserved for next stage. Thus, the speed of shot detection was improved by r
... Show MoreThe smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, rec
... Show MoreLowpass spatial filters are adopted to match the noise statistics of the degradation seeking
good quality smoothed images. This study imply different size and shape of smoothing
windows. The study shows that using a window square frame shape gives good quality
smoothing and at the same time preserving a certain level of high frequency components in
comparsion with standard smoothing filters.
The denoising of a natural image corrupted by Gaussian noise is a problem in signal or image processing. Much work has been done in the field of wavelet thresholding but most of it was focused on statistical modeling of wavelet coefficients and the optimal choice of thresholds. This paper describes a new method for the suppression of noise in image by fusing the stationary wavelet denoising technique with adaptive wiener filter. The wiener filter is applied to the reconstructed image for the approximation coefficients only, while the thresholding technique is applied to the details coefficients of the transform, then get the final denoised image is obtained by combining the two results. The proposed method was applied by usin
... Show MoreA new de-blurring technique was proposed in order to reduced or remove the blur in the images. The proposed filter was designed from the Lagrange interpolation calculation with adjusted by fuzzy rules and supported by wavelet decomposing technique. The proposed Wavelet Lagrange Fuzzy filter gives good results for fully and partially blurring region in images.