Abstract:
Denoising smooths noisy image super-resolution (SR), making it difficult. IBP can improve the reconstructed SR image, but there is no clean reference image. This paper introduces a noisy image SR back-projection algorithm.
It seeks LR-SR image consistency. Using noisy and denoised reconstruction errors, we estimate the clean reconstruction error to back-project. To estimate clean reconstruction error, we create a new cost function on the PCA transform domain.
Texture probability is used to region-adaptively combine noisy and denoised reconstruction errors in the cost function data term. Based on reconstruction error Laplacian characteristics, the sparsity constraint is added to the regularization term.
We suggest eigenvector estimation to reduce noise. The experimental results show that the proposed method performs back-projection more noise-robustly than the conventional IBP and works well with other SR methods as post-processing.
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