A novel multi-image super-resolution reconstruction method using anisotropic fractional order adaptive norm

Diffusion‐steered super‐resolution method based on the Papoulis–Gerchberg algorithm

“…Finally, we compared super‐resolution results of the new method and some state‐of‐the‐art classical methods: modified PG (PG‐SR) [18], TV [35], spatial adaptive prior [13], LA L 1 − L 2 ‐norm (LA‐SR) [5], anisotropic FO (FO‐SR) [7], and sparse coding (SC‐SR) [8]. Figs.…”

“…Super‐resolution refers to an image restoration problem that seeks to recover a high‐quality image from several degraded versions [1–8]. The process requires that pairs of input frames contain relative motions, usually rotations and translations, between them.…”

Super-resolution Reconstruction of Noisy Video Image Based on Sparse Representation Algorithm

“…The experimental results of subjective evaluation and objective evaluation proved the effectiveness of this method. Chen et al [7] designed a regularization model based on the anisotropic fractional order adaptive (AFOA) specification, applied it to SRR image processing, and found that the model could achieve adaptive removal of image noise and well protect image edges. The experimental results showed that the image quality obtained by this method was good.…”

Noise-robust boundary recursive algorithm for super-resolution reconstruction of staring focal plane array micro-scanning imaging

“…The benefits of the MSRR methods are that they not only increase the sampling rate but also reduce the pixel integral blur effect. However, current MSRR methods are time-consuming because the calculation is complicated and the number of iterations required to converge is significant [19]. This makes MSRR methods difficult to apply to real-time micro-scanning imaging systems.…”