Adaptive multi-dimension sparsity based coefficient estimation for compression artifact reduction

Sparsity has shown promising results in various image restoration applications. Recent advances have suggested that structured or group sparsity often leads to more powerful results in compression artifact reduction studies. In this paper, we introduce nonlocal multi-dimension sparsity in an adaptive space-transform domain, which performs multi-scale wavelet transform on DCT coefficients of similar patches. The new transform efficiently reduces image redundancies between inner block and inter block simultaneously, thus it can substantially achieve sparse representation for images. Furthermore, a band-based filter is proposed to reduce compression artifacts by shrinking transform coefficients adaptively. Because of the overlapped processing, adaptive aggregation is used to combine different estimates for each block. The proposed algorithm achieves improvement over some methods in terms of both objective and subjective qualities.