Image and video enhancement through motion based interpolation and nonlocal-means denoising techniques

In this research, we investigate advanced image and video enhancement techniques based on motion based interpolation and nonlocal-means (NL-means) denoising. The dissertation consits of three main results. Two video processing applications namely, video error concealment (EC) and frame rate up-conversion (FRUC), based on motion analysis have been examined. Then, an improved NL-means algorithm have been proposed for image denoising. They are detailed below.In the first part of this study, low-complexity error concealment techniques are studied. The boundary matching algorithm (BMA) is an attractive choice for video error concealment due to its low complexity. Here, we examine a variant of BMA called the outer boundary matching algorithm (OBMA). Although BMA and OBMA are similar in their design principle, it is empirically observed that OBMA outperforms BMA by a significant margin (typically, 0.5dB or higher) while maintaining the same level of complexity. We first explain the superior performance of OBMA, and conclude that OBMA provides an excellent tradeoff between the complexity and the quality of concealed video for a wide range of test video sequences and error conditions. In addition, we present two extensions of OBMA, i.e. refined local search and multiple boundary layers. These extensions can be employed to enhance the performance of OBMA at slightly higher computational complexity. Finally, the effect of the exible macroblock ordering (FMO) on the performance of several EC algorithms is examined.In the second part of this work, two challenging situations for video frame rate upconversion (FRUC) are identified and analyzed namely, when the input video clip has abrupt illumination change and a low frame rate. Then, a low-complexity processing technique and robust FRUC algorithm are proposed to address these two issues. The proposed algorithm utilizes a translational motion vector model of the first- and the second-orders and detects the continuity of these motion vectors. Additionally, a spatial smoothness criterion is employed to improve perceptual quality of interpolated frames. The superior performance of the proposed algorithm has been extensively tested and representative examples are given in this work.In the third part of this research, an adaptive image denoising technique based on the NL-means algorithm is proposed. The proposed method employs the singular value decomposition (SVD) method and the K-means clustering (K-means) technique to achieve robust block classification in noisy images. Then, a local window is adaptively adjusted to match the local property of a block. Finally, a rotated block matching algorithm based on the alignment of dominant orientation is adopted for similarity matching. In addition, the noise level can be accurately estimated using block classification and the Laplacian operator. Experimental results are given to demonstrate the superior denoising performance of the proposed adaptive NL-means (ANL-means) denoising technique over various image denoising benchmarks in term of both PSNR and perceptual quality comparison, where images corrupted by additive white Gaussian noise (AWGN) and Rician noise are both tested.