Abstract:
This Digital Image Processing Project introduces the rank residual constraint (RRC) model for rank minimization. We minimize the rank residual to progressively approximate the underlying low-rank matrix, unlike nuclear norm minimization (NNM) and weighted nuclear norm minimization (WNNM), which estimate the matrix directly from corrupted observations.
We use the image nonlocal self-similarity (NSS) prior with the proposed RRC model to restore images, including denoising and compression artifact reduction. We first use the image NSS prior to get a good reference of the original image groups, then minimize the rank residual of the image groups between this reference and the degraded image to get a better estimate of the desired image.
Each iteration gradually updates the reference and estimated image. We theoretically analyze the RRC model’s feasibility using the group-based sparse representation model. Experimental results show that the RRC model outperforms many state-of-the-art schemes in objective and perceptual quality.
Also Read:
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- From Pairwise Comparisons and Rating to a Unified Quality Scale
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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