The reconstruction results obtained by FLBM has slightly better appearances than ALBM, FLBI, LBM and BP, and has better appearances than OMP.
Table 2 gives the quantitative results of the FLBM, ALBM, FLBI, LBM, BP and OMP algorithms.
However, the FLBM is significantly superior to the LBM, BP and OMP algorithm, and is slightly superior to the ALBM and FLBI algorithm, for the same sampling rate.
To confirm the universality of the proposed FLBM algorithm, we apply it now to reconstruct the three different groups of the test images.
To illustrate the FLBM robust to noise, a zero-mean
The proposed FLBM reconstruction algorithm is applied to these test images with Gaussian
The reconstruction results obtained by FLBM and BP have slightly better appearances than ALBM, FLBI and LBM, and have better appearances than OMP.
To confirm the robustness of the proposed FLBM algorithm, the PSNR (dB) is used to measure the performance of the proposed algorithm for the noisy Lena image (256 x 256).
Moreover, our proposed FLBM algorithm outperforms BP and OMP algorithm and slightly outperforms ALBM, FLBI and LBM in terms of the PSNR at the same noise levels.
For further comparison, the PSNR in dB of the reconstructed different noisy images at different noise levels resulting from the FLBM and ALBM algorithms are listed in Table 6.