From the beginning of producing the TV and video frame broadcasting, the problem of reducing both of the video data and needed bandwidth while maintaining image quality and fast enough for the refreshing of images on the screen was raised. For this purpose interlace sampling technique was developed.In the interlaced images, only odd or even lines of frames are scanned, alternately. In fact, if interlacing is defined as a kind of spatial–temporal sampling of video images, de-interlacing to omit those artifacts that remain on images as a result of this kind of sampling would do reverse of down sampling. De-interlacing has become essential with the advent of digital technology to video monitor manufacturing and its dramatic increase in the world market since digital monitors are not able to display interlace images. Regarding the growing importance of this issue in last years, many techniques have been proposed. Most of de-interlacing methods attempt to eliminate the artefacts caused by interlacing, by using of spatial or temporal information. Some of these methods, with the aim of creating a better quality of images, use motion compensation algorithms. Because of high complexity of the motion compensation algorithms, most applications have hesitation in use of these methods. De-interlacing is easily possible for stable image sequence, whereas the existence of motion in video sequence and changes in video image lights not only causes annoying visual effects for reconstructed images, but also impose more complexity to different methods. However, implementing de-interlace in T.V broadcast in real time, makes issues such as less complexity and saving in show time more crucial. For this reason there are many challenges among proposed methods. Consequently, the main concerns of proposed methods are eliminating annoying effects of interlaced information while conserving simplicity and avoiding their prolonged processes. In this study the researcher proposed three motion adaptive methods in each of which the best way to reconstruct missing pixels based on their motion conditions. The results of simulation and comparing to other similar proposed methods are acceptable both in R quantity and visual quality criterion. Keywords: Interlacing, De-interlacing, Motion Adaptive Method, Optical Flow
