Recently, specially crafted unidimensional optimization has been successfully used as line search to accelerate the overrelaxed and monotone fast iterative shrinkage-threshold algorithm (OMFISTA) for computed tomography. In this paper, we extend the use of fast line search to the monotone fast iterative shrinkage-threshold algorithm (MFISTA) and some of its variants. Line search can accelerate the FISTA family considering typical synthesis priors, such as the ℓ 1 -norm of wavelet coefficients, as well as analysis priors, such as anisotropic total variation. This paper describes these new MFISTA and OMFISTA with line search, and also shows through numerical results that line search improves their performance for tomographic high-resolution image reconstruction.

Infrared focal-plane array (IRFPA) detectors suffer from fixed-pattern noise (FPN) that degrades image quality, which is also known as spatial nonuniformity. FPN is still a serious problem, despite recent advances in IRFPA technology. This paper proposes new scene-based correction algorithms for continuous compensation of bias and gain nonuniformity in FPA sensors. The proposed schemes use recursive least-square and affine projection techniques that jointly compensate for both the bias and gain of each image pixel, presenting rapid convergence and robustness to noise. The synthetic and real IRFPA videos experimentally show that the proposed solutions are competitive with the state-of-the-art in FPN reduction, by presenting recovered images with higher fidelity.

Infrared (IR) focal-plane array (FPA) detectors suffer from fixed-pattern noise (FPN), also known as spatial nonuniformity, which degrades image quality. In fact, FPN remains a serious problem despite recent advances in IRFPA technology. This work proposes a scene-based correction algorithm to continuously compensate for bias and gain nonuniformity in focal-plane array sensors. The proposed technique is a recursive algorithm based on recursive least square (RLS) techniques that jointly compensates for both bias and gain for each image pixel. The method converges rapidly and presents robustness to noise. Experiments with synthetic and real IRFPA videos has shown that it is competitive with the state-of-the-art in FPN reduction, presenting recovered images with higher fidelity when compared to them.