14,626 research outputs found
Multimodal Image Denoising based on Coupled Dictionary Learning
In this paper, we propose a new multimodal image denoising approach to
attenuate white Gaussian additive noise in a given image modality under the aid
of a guidance image modality. The proposed coupled image denoising approach
consists of two stages: coupled sparse coding and reconstruction. The first
stage performs joint sparse transform for multimodal images with respect to a
group of learned coupled dictionaries, followed by a shrinkage operation on the
sparse representations. Then, in the second stage, the shrunken
representations, together with coupled dictionaries, contribute to the
reconstruction of the denoised image via an inverse transform. The proposed
denoising scheme demonstrates the capability to capture both the common and
distinct features of different data modalities. This capability makes our
approach more robust to inconsistencies between the guidance and the target
images, thereby overcoming drawbacks such as the texture copying artifacts.
Experiments on real multimodal images demonstrate that the proposed approach is
able to better employ guidance information to bring notable benefits in the
image denoising task with respect to the state-of-the-art.Comment: 2018 IEEE International Conference on Image Processing (ICIP). arXiv
admin note: text overlap with arXiv:1806.0988
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Restoration
In this paper, we propose a novel approach to the rank minimization problem,
termed rank residual constraint (RRC) model. Different from existing low-rank
based approaches, such as the well-known nuclear norm minimization (NNM) and
the weighted nuclear norm minimization (WNNM), which estimate the underlying
low-rank matrix directly from the corrupted observations, we progressively
approximate the underlying low-rank matrix via minimizing the rank residual.
Through integrating the image nonlocal self-similarity (NSS) prior with the
proposed RRC model, we apply it to image restoration tasks, including image
denoising and image compression artifacts reduction. Towards this end, we first
obtain a good reference of the original image groups by using the image NSS
prior, and then the rank residual of the image groups between this reference
and the degraded image is minimized to achieve a better estimate to the desired
image. In this manner, both the reference and the estimated image are updated
gradually and jointly in each iteration. Based on the group-based sparse
representation model, we further provide a theoretical analysis on the
feasibility of the proposed RRC model. Experimental results demonstrate that
the proposed RRC model outperforms many state-of-the-art schemes in both the
objective and perceptual quality
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