We propose a fast and robust 2D-affine global motion estimation algorithm based on phase-correlation in the Fourier-Mellin domain and robust least square model fitting of sparse motion vector field and its application for digital image stabilization. Rotation-scale-translation (RST) approximation of affine parameters is obtained at the coarsest level of the image pyramid, thus ensuring convergence for a much larger range of motions. Despite working at the coarsest resolution level, using subpixel-accurate phase correlation provides sufficiently accurate coarse estimates for the subsequent refinement stage of the algorithm. The refinement stage consists of RANSAC based robust least-square model fitting for sparse motion vector field, estimated using block-based subpixel-accurate phase correlation at randomly selected high activity regions in finest level of image pyramid. Resulting algorithm is very robust to outliers such as foreground objects and flat regions. We investigate the robustness of the proposed method for digital image stabilization application. Experimental results show that the proposed algorithm is capable of estimating larger range of motions as compared to another phase correlation method and optical flow algorithm.

We propose total subset variation (TSV), a convexity preserving generalization of the total variation (TV) prior, for higher order clique MRF. A proposed differentiable approximation of the TSV prior makes it amenable for use in large images (e.g. 1080p). A convex relaxation of sub-exponential distribution is proposed as a criterion to determine the parameters of the optimization problem resulting from the TSV prior. For the super-resolution application, experiments show reconstruction error improvement with respect to the TV and other methods.

Support vector machine (SVM) regression is considered for a statistical method of single frame superresolution in both the spatial and discrete cosine transform (DCT) domains. As opposed to current classification techniques, regression allows considerably more freedom in the determination of missing high-resolution information. In addition, since SVM regression approaches the superresolution problem as an estimation problem with a criterion of image correctness rather than visual acceptableness, its optimization results have better mean-squared error. With the addition of structure in the DCT coefficients, DCT domain image superresolution is further improved

In this paper, we propose a compression artifact reduction algorithm based on ν support vector regression. It belongs to the broad family of regularized reconstruction methods but regularization model is learned from a set of training samples of original images and corresponding noise corrupted version. As opposed to artifact reduction methods specific to each type of compression artifact (e.g. blocking, ringing etc), we treat such different artifacts as symptoms of the same problem, quantization of DCT coefficients. In the testing step, algorithm tries to undo the effect of quantization using information (relationship between original and artifact-corrupted image) learned during the training step. Experimental results exhibit significant reduction in all types of compression artifacts.

This paper proposes the application of learned kernels in support vector regression to superresolution in the discrete cosine transform (DCT) domain. Though previous works involve kernel learning, their problem formulation is examined to reformulate the semi-definite programming problem of finding the optimal kernel matrix. For the particular application to superresolution, downsampling properties derived in the DCT domain are exploited to add structure to the learning algorithm. The advantage of the proposed method over other learning-based superresolution algorithms include specificity with regard to image content, structured consideration of energy compaction, and the added degrees of freedom that regression has over classification-based algorithms

A lot of research has been conducted on motion-compensated (MC) de-interlacing, but there are very few publications that discuss the performances of de-interlacing quantatively. The various methods are compared through their performance on known video sequences. Linear system analysis of interlaced video and de-interlacer are proposed in. It is well established that the performance of the MC methods outperform the fixed or motion-adaptive methods when the motion vectors used are reliable and true to the scene content. Being an open-loop process the performance of the MC de-interlacers degrade drastically when there are motion vector errors. In this paper, a linear system analysis of MC video upconversion systems is presented and the effects of motion vector accuracy on system performance are analyzed. We investigate the various factors that contribute to the motion vector inaccuracy, such as incorrect motion modelling, acceleration between the frames, and insufficient interpolation kernel.

In this paper we investigate the application of texture synthesis and image inpainting techniques for video applications. Working in the non-parametric framework, we use 3D patches for matching and copying. This ensures temporal continuity to some extent which is not possible to obtain by working with individual frames. Since, in present application, patches might contain arbitrary shaped and multiple disconnected holes, fast Fourier transform (FFT) and summed area table based sum of squared difference (SSD) calculation (S.L. Kilthau, et al, 2002) cannot be used. We propose a modification of above scheme which allows its use in present application. This results in significant gain of efficiency since search space is typically huge for video applications.

We propose a fast and robust global motion estimation (GME) algorithm, based on a 2-stage coarse-to-fine refinement strategy, which is capable of measuring large motions. A 6-parameter affine motion model has been used. Coarse estimation is carried out in the frequency domain using polar, log-polar or log-log sampling of the Fourier magnitude spectrum of the sub-sampled image. The sampling scheme is adaptively selected, based on past motion patterns. The refinement stage consists of a RANSAC based model fitting to motion vectors of randomly selected high-activity blocks, and hence is robust to outliers. The motion vector of blocks is measured using phase correlation, which offers two advantages in this context: sub-pixel accuracy without significant computational overhead; and if a particular block consists of background as well as foreground pixels, both motions are simultaneously measured. Due to its hardware-friendly nature, the proposed algorithm holds potential for real-time GME even for television images.

A DCT-based phase correlation motion estimation algorithm is proposed in this paper. By combining four real transforms, a new complex linear phase transform is obtained and is used in phase correlation motion estimation. The application in video compression is discussed in detail. The simulation results show that the proposed algorithm is robust and efficient.