Hybrid higher‐order total variation model for multiplicative noise removal

Fast algorithm for box‐constrained fractional‐order total variation image restoration with impulse noise

“…Although the first‐order TV is very efficient in preserving sharp edges, it also causes some undesirable effects, the so‐called staircase effects. To alleviate this side effect of TV, some high‐order TV models are proposed, such as second‐order TV [21], second‐order total generalized variation model [22] and the hybrid variation model [23] and so on. However, high‐order TV models are prone to produce speckle‐like noise.…”

Total fractional-order variation regularization based image reconstruction method for capacitively coupled electrical resistance tomography

“…Mathematically, the problem of total variation method can be addressed by introducing higher-order or fractional-order derivatives. The success of these methods has been demonstrated in the field of image processing [22]- [24]. In this work, a novel image reconstruction method based on total fractional-order variation regularization(TFVR) is proposed for recovering conductivity distribution in CCERT.…”

Hybrid High-Order and Fractional-Order Total Variation with Nonlocal Regularization for Compressive Sensing Image Reconstruction

“…Adaptive weighted high-frequency iterative fractional-order TV is proposed, the high frequency gradient of an image is reweighted in iterations adaptively when using fractional-order TV [21]. Another conventional way to suppress the staircase artifact is to use a high-order TV regularization [22,23], there exists a high-order total variation minimization model that removes undesired artifacts for restoring blurry and noisy images [24]. High-order TV regularization can reconstruct piecewise linear regions, but high-order TV may also smooth out the image details, and it may reduce the ability of edges-preserving [23].…”

“…Another conventional way to suppress the staircase artifact is to use a high-order TV regularization [22,23], there exists a high-order total variation minimization model that removes undesired artifacts for restoring blurry and noisy images [24]. High-order TV regularization can reconstruct piecewise linear regions, but high-order TV may also smooth out the image details, and it may reduce the ability of edges-preserving [23]. In order to make full use of image prior information as much as possible to construct a regularized model.…”