abstract: Video deinterlacing is a key technique in digital video processing, particularly with the widespread usage of LCD and plasma TVs. This thesis proposes a novel spatio-temporal, non-linear video deinterlacing technique that adaptively chooses between the results from one dimensional control grid interpolation (1DCGI), vertical temporal filter (VTF) and temporal line averaging (LA). The proposed method performs better than several popular benchmarking methods in terms of both visual quality and peak signal to noise ratio (PSNR). The algorithm performs better than existing approaches like edge-based line averaging (ELA) and spatio-temporal edge-based median filtering (STELA) on fine moving edges and semi-static regions of videos, which are recognized as particularly challenging deinterlacing cases. The proposed approach also performs better than the state-of-the-art content adaptive vertical temporal filtering (CAVTF) approach. Along with the main approach several spin-off approaches are also proposed each with its own characteristics. / Dissertation/Thesis / M.S. Electrical Engineering 2012

This document elaborates on the subject of video interlacing and its removal. It describes the interlacing of video, its history and the reasons that led to its use. The document also explains why it is necessary to remove interlacing and the basic methods that are used for it. It describes the proposed deinterlacing algorithm and its implementation, including description of inpainting and block matching. Included are also test results of both quality and speed of my deinterlacing algorithm. The final chapter describes the implementation as a console application and a DLL library.

Interlacing is the process of frame scanning and frame displaying being based on the even and uneven lines‘ interchanging. This framework originated along with the PAL and NTSC television standards‘ formation in the course of 1930s. The process mentioned above stopped frames displayed on older TV sets from flashing. Nevertheless, there is a disadvantage of the frame processed after this manner, consisting in its attribute to be displayed in a clean-cut way just on classical TV sets. However, plasma and LCD television appliances, projectors and computer monitors always display the entire frame all at once, therefore composing it of the both fields, yet not being acquired concurrently, differing in moving objects‘ positions, moreover their contours are doubled, frayed and unfocussed. This master´s thesis deals with fundamentals of the interlaced video signal‘s formation, problems coming about at featuring the interlaced video like this, followed by operating methods of deinterlacing itself. In conclusion, there are my own softwares presented, having been developed in MATLAB computer environment and software command language C, and implementing some of the operating methods designed for deinterlacing in uncompressed digital video sequences.

<p>In this thesis we present new methods for image and video super resolution and video deinterlacing. For image super resolution a new approach for finding a High Resolution (HR) image from a single Low Resolution (LR) image has been introduced. We have done this by employing Compressive Sensing (CS) theory. In CS framework images are assumed to be sparse in a transform domain such as wavelets or contourlets. Using this fact we have developed an approach in which the contourlet domain is considered as the transform domain and a CS algorithm is used to find the high resolution image. Following that, we extend our image super resolution scheme to video super resolution. Our video super resolution method has two steps, the first step consists of our image super resolution method which is applied on each frame separately. Then a post processing step is performed on estimated outputs to increase the video quality. The post processing step consists of a deblurring and a Bilateral Total Variation (BTV) filtering for increasing the video consistency. Experimental results show significant improvement over existing image and video super resolution methods both objectively and subjectively.</p> <p>For video deinterlacing problem a method has been proposed which is also a two step approach. At first 6 interpolators are applied to each missing line and the interpolator which gives the minimum error is selected. An initial deinterlaced frame is constructed using selected interpolator. In the next step this initial deinterlaced frame is fed into a post processing step. The post processing step is a modified version of 2-D Bilateral Total Variation filter. The proposed deinterlacing technique outperforms many existing deinterlacing algorithms.</p> / Master of Science (MSc)