Image flicker, the unnatural temporal fluctuation in perceived image intensity, is an annoying artifact existing in many old film sequences and its removal is an integral part in any old film restoration process. In this paper, we propose an improved flicker removal method based on flicker parameters estimation with the aide of motion vectors compensation. We tested the proposed method on sequences with artificially added global and local flicker, as well as on natural real flicker film material. Test results obtained showed the proposed method produced much better flicker correction quality than an established flicker removal method known as the "Roosmalen method".

This paper presents new MRF (Markov random field) models in a bidirectional Bayesian framework for accurate motion and occlusion field estimation. With careful selection of the five free parameters required by the models, good experimental results have been obtained. The resultant computational speed is also 5.5 times faster compared with the conventional "iterated conditional mode" relaxation using the proposed fast bidirectional relaxation.

It is well known that accurate dense motion field can improve the video coding efficiency. This paper presents a novel Markov random field (MRF) model that estimates both the dense motion and uncovered background fields in image sequences, and the application of these estimates in H.263-based video coding framework.

Bayesian motion estimation requires two PDF models: observation model and motion field (prior) model. The optimization process for this method uses a sequential approach, e.g. simulated annealing. This paper proposes an adaptive blocksize observation model and multiscale regularization for the prior model and the optimization process. The purposes are to increase the speed and to improve the result. The proposed framework can initialize the Bayesian method. The result in this paper shows one of the possibility of its usage. Many strategies can be derived from this framework to work for itself or to support the Markov random field modeling for motion estimation.

This paper proposes a robust spatial-temporal MRF model based scheme for aggressive detection and accurate interpolation of missing data (blotches) in highly corrupted image sequences. The blotches in noise-corrupted image sequences exhibit a temporal discontinuity characteristic, which is used for the detection of blotches. The MRF model addresses the problem of incorrect detection due to poor motion compensation at moving edges, by incorporating a moving-edge detector into a priori model. In highly corrupted image sequences where an aggressive detector is needed, the detection field can be interpreted as three main classes. This classification allows for effective noise-removal. These regions are blotches that are to be interpolated: (a) with the existing motion vector field, (b) requiring motion vector correction, and other (c) falsely detected regions. This results in a novel scheme that effectively subdues noise without corrupting other areas of heavily distorted image sequences.

Gibbs-Markov random field (GMRF) modeling has been shown to be a robust method in the detection of missing-data in image sequences for a video restoration application. However, the maximum a posteriori probability (MAP) estimation of the GMRF model requires computationally expensive optimization algorithms in order to achieve an optimal solution. The continuous relaxation labeling (RL) is explored in this paper as an efficient approach for solving the optimization problem. The conversion of the original combinatorial optimization into a continuous RL formulation is presented. The performance of the RL formulation is analyzed and compared with that of other optimization methods such as stochastic simulated annealing, iterated conditional modes, and mean field annealing. The results show that RL holds out promise as an optimization algorithm for problems in image sequence processing.

This paper proposes a new spatial-temporal Markov random field (MRF) model for the detection of missing data (also referred to as blotches) in image sequences. The blotches in noise-corrupted image sequences exhibit a temporal discontinuity characteristic that is commonly used for the detection of blotches. However, badly motion-compensated pixels also appear as temporal discontinuities, making it difficult to distinguish the true blotches from the poorly motion-compensated regions. The proposed MRF model addresses the problem of incorrect detection due to poor motion compensation at the moving edges. It is found that the degree of incorrect detection (at the moving edges) in image sequences is reduced significantly by incorporating a moving-edge detector into the MRF model.

A low-complexity vector propagation (VP) algorithm is introduced for the estimation of bidirectional motion vector fields in image sequences. The proposed VP algorithm exploits the strong correlation between forward and backward motion vector fields in image sequences. The performance of the VP algorithm is compared to that of a bidirectional multiresolution block-matching (MRBM) motion estimation (ME) algorithm. Computer simulation results demonstrate that with the VP algorithm, the computational workload of the bidirectional ME is reduced by a factor of nearly 2. The robustness of the VP algorithm is extensively tested using computer generated image sequences and real movies for a motion picture restoration (MPR) system. It is shown that the VP algorithm is robust enough to be used in the computationally demanding MPR algorithm, in which the performance of the novel VP algorithm is close to that of a bidirectional MRBM ME algorithm. With this technique, other video processing systems that desire to take advantage of bidirectional motion estimation can now do so without an excessive increase in computing time.

This paper proposes a new spatial-temporal MRF model for the detection of missing data (also referred to as blotches) in image sequences. The blotches in noise-corrupted image sequences exhibit a temporal discontinuity characteristic which is commonly used for the detection of blotches. However, the badly motion compensated pixels will also appear as temporal discontinuities, thus making it difficult to distinguish the true blotches from the poorly motion compensated regions. The proposed MRF model addresses the problem of incorrect detection. It is found that the degree of false-alarm in the detection of the blotches in image sequences can be reduced by using a moving-edge detector in the MRF model to identify the blotch-edges from the moving edges.

This paper proposes a new AR model-based restoration algorithm which is able to suppress mixed noise processes and recover lost signals in an image sequence. A drawback of an AR model is the limiting size of the block (of pixels) that can be adequately modeled. Using a single set of AR coefficients to restore a large region of missing data will result in a homogenized texture region. In order to overcome this inherent limitation of the AR model, a block-based divide-and-conquer approach is proposed. In addition, a new Gaussian weighting scheme is used to better estimate the AR coefficients for the interpolation process.

This paper proposes a new Markov random field (MRF) model for the detection of occluded regions in image sequences. Motion vectors are not defined in an occluded region, thus the regions with high motion compensated prediction error are commonly regarded as occluded regions. However, badly motion compensated pixels will also appear as occluded pixels, making it difficult to distinguish the true occluded pixels from the poorly motion compensated regions. The proposed MRF model addresses this problem by incorporating motion information into the occlusion model. This is derived from the observation that occlusion occurs when objects move. It is found that an accurate occlusion region can be detected and better motion estimation can be made with the new model using iterated conditional modes (ICM).

Most image restoration algorithms will blur edges of moving objects in the vicinity of occluded and uncovered image regions. These artifacts are visually annoying. This paper proposes a new bi-directional 3D auto-regressive model which is able to suppress mixed noise processes and recover lost signals in both the covered and uncovered regions in an image sequence. The performance of this algorithm is shown to be superior to the existing 3D AR model.

This paper aims to develop effective parallel processing techniques for 3-dimensional (3D) multi-level median filtering (3DMMF) with motion compensation on video sequences. Due to the nature of unbalanced load of the filtering algorithm and the objective for the parallel approach, two methods of dynamic load balancing have been proposed and compared. They are sender-initiated-load-balancing (SILB) and receiver-initiated-load-balancing (RILB) algorithms. We propose a SILB algorithm which utilises the spatial-temporal characteristics of the processed sequences for load prediction to achieve dynamic load balancing. Both theoretical analysis and experimental results on the IBM SP2 computing surface have been presented in this paper.