Compressed image restoration based on edge enhancement field of experts Images encoded at low bit rate may suffer from blocking artifacts, which dramatically degrade the visual quality. In this paper, we propose a postprocessing image restoration scheme for JPEG compressed images based on the maximum a posteriori criterion. A degradation model, represent by additive Gaussian noise model, is proposed to simulate JPEG compression process, while the original image is modeled as a high order Markov random field (MRF) based on the fields of experts framework. Meanwhile, an enhancement algorithm is proposed to restore the high frequency (HF) components. Experiment results have demonstrated that the proposed scheme can reproduce higherquality images in terms of both objective and subjective quality.

Restoration of compressed video using temporal information This paper proposes a Bayesian method for the restoration of video sequences compressed using the discrete cosine transform (DCT). Two elements, both part of the Bayesian observation model, distinguish the proposed algorithm from the majority of other methods in the literature. The proposed algorithm incorporates temporal information from nearby framespast, present and futurewhen forming an estimate of the current frame. Furthermore, this work uses a spatiallyvarying noise model to account for the noise introduced by quantization of the DCT coefficients. These two aspects of the observation model are used in conjunction with a HuberMarkov Random Field (HMRF) model to form a Bayesian estimate of each frame in the compressed video sequence.