Multiresolution example-based depth image restoration

In this paper we present a new method for superresolution of depth video sequences using high resolution color video. Here we assume that the depth sequence does not contain outlier points which can be present in the depth images. Our method is based on multiresolution decomposition, and uses multiple frames to search for a most similar depth segments to improve the resolution of the current frame. First step is the wavelet decomposition of both color and depth images. Scaling images of the depth wavelet decomposition, are superresolved using previous and future frames of the depth video sequence, due to their different nature. On the other side wavelet band are improved using both previous frames of the wavelet bands and wavelet bands of color images since similar edges might appear in both images. Our method shows significant improvements over some recent depth images interpolation methods

Single frame super-resolution image system

The estimation of some unknown quantity information from known observable information can be viewed as a specific statistical process which needs an extra source of information prediction strategy. In this regard, image super-resolution is an important application In this thesis, we proposed a new image interpolation method based on Redundant Discrete Wavelet Transform (RDWT) and self-adaptive processes in which edge direction details are considered to solve single-frame image super-resolution task. Information about sharp variations, both in horizontal and vertical directions derived from wavelet transform sub-bands are considered, followed by detection and modification of the aliasing part in the preliminary output in order to increase the visual effect. By exploiting fundamental properties of images such as property of edge direction, different parts of the source image are considered separately in order to predict the vertical and horizontal details accurately, helping to consummate the whole framework in reconstructing the high-resolution image. Extensive tests of the proposed method show that both objective quality (PSNR) and subjective quality are obviously improved compared to several other state-of-the-art methods. And this work also leaved capacious space for further research, not only theoretical but also practical. Some of the related research applications based on this algorithm strategy are also briefly introduced

Video Super Resolution

Note: appendices for this title available here. Advances in digital signal processing technology have created a wide variety of video rendering devices from mobile phones and portable digital assistants to desktop computers and high definition television. This has resulted in wide diversity of video content with spatial and temporal properties fitting into their intended rendering devices. However the sheer ubiquity of video content creation and distribution mechanisms has effectively blurred the classification line resulting in the need for interchangeable rendering of video content across devices of varying spatio-temporal properties. This results in a need for efficient and effective conversion techniques; mostly to increase the resolution (referred to as super resolution) in-order to enhance quality of perception, user satisfaction and overall the utility of the video content

Edge-preservation resolution enhancement with oriented wavelets

A novel directionally adaptive image resolution enhancement method is proposed. The method uses a multiple-direction wavelet transform, called directionlets, to efficiently extract edge information along different directions, not necessarily horizontal or vertical, from the low-resolution image. Then, the high-resolution image is synthesized using the extracted information to preserve sharpness of edges and texture. The novel algorithm provides the interpolated images at a higher resolution that are better than the images obtained by the state-of-the-art methods in terms of both numeric and visual qualit