Breaking the limitations of the traditional Shanon-Nyquist sampling theorem

compressed sensing is a recent paradigm

which allows a signal to be sampled at sub-Nyquist rates and proposed a methodology of recovery that incurs no loss. The field of compressed sensing is related to other topics in signal processing. Especially

imaging techniques having a strong affinity with compressed sensing include coded aperture and computational photography. In recent years

compressed sensing image reconstruction has caused widespread concern

and the total variation (TV) regularization

which describes the sparsity of the image gradient

has been widely used for image reconstruction. Inspired by these ideas

we propose a novel compound regularized compressed sensing image reconstruction model based on optimal reweighted TV. Our reconstruction modeling is based on the classical TV regularization recovery model

and some actions have been taken to improve the reconstruction performance. At first

the TV regularization always results in a piecewise constant solutions. This will make the reconstructed image too smooth and some details

such as edges and textures

are lost. To overcome this drawback

the gradient information of the image is utilized to estimate weights

and build a reweighted TV-based compressed sensing image reconstruction model. Then

for reducing the noise or other degradation influence

we introduce a TV denoising (Rudin-Osher-Fatemi

ROF)model into the optimal estimation of weights. Next

the characters of the natural image are introduced into image modeling as the priors such as the nonlocal structure similarity and local regression priors. We introduce these priors into the reconstruction model to preserve the image details

and propose a novel compound regularized optimization model based on optimal reweighted TV. At last

the optimization model could be reduced to a series of convex minimization problems that can be efficiently solved with a combination of the projection method and operator splitting method

leading to fast and easy-to-code algorithms. In the conventional TV regularization reconstruction model

there is not enough prior information which is utilized to represent different characters of the natural image. Therefore

a compound regularized model is proposed to use these corresponding priors of different characters in this paper. The experimental results demonstrate the more refined image reconstruction in our proposed method. Especially

the sparsity prior of the image gradient is fully considered as reweighted TV regularizer

which could guarantee the reconstructed effects of the smoothed areas and strong edges

while the nonlocal structure similarity and local regression priors are also exploited to improve the reconstructed performance of weak edges or textures. TV regularization model is a widely-used compressed sensing image reconstruction method. However

this process is easy to be affected by noise or other degradation. Meanwhile

it is difficult to obtain refined reconstruction result because image prior information is rarely utilized. We propose a novel compound regularized compressed sensing image reconstruction model. Here

we integrate the sparsity prior

the nonlocal structure similarity prior and the local regression prior into reconstruction model as compound regularizers. Extended experiment results indicate that the proposed compressed sensing reconstruction method has a better improvement in terms of objective criterion and visual fidelity over other related TV-based reconstruction methods.