Abstract
This paper presents two new models for solving image the deblurring problem in the presence of impulse noise. One involves a high-order total variation (TV) regularizer term in the corrected total variation L1 (CTVL1) model and is named high-order corrected TVL1 (HOCTVL1). This new model can not only suppress the defects of the staircase effect, but also improve the quality of image restoration. In most cases, the regularization parameter in the model is a fixed value, which may influence processing results. Aiming at this problem, the spatially adapted regularization parameter selection scheme is involved in HOCTVL1 model, and spatially adapted HOCTVL1 (SAHOCTVL1) model is proposed. When dealing with corrupted images, the regularization parameter in SAHOCTVL1 model can be updated automatically. Many numerical experiments are conducted in this paper and the results show that the two models can significantly improve the effects both in visual quality and signal-to-noise ratio (SNR) at the expense of a small increase in computational time. Compared to HOCTVL1 model, SAHOCTVL1 model can restore more texture details, though it may take more time.
Highlights
In the field of electronics and information, signal processing is a hot research topic, and as a special signal, the study of image has attracted the attention of scholars all over the world [1,2,3].In image processing, image restoration is one of the most important issues and this issue has received extensive attention in the past few decades [4,5,6,7,8,9,10,11]
The high-order corrected TVL1 (HOCTVL1) model is compared with TVL1 [20], HTVL1 [31], corrected total variation L1 (CTVL1) [23], four state-of-the-art methods are selected for comparisons and the methods include LpTV-alternating direction method of multipliers (ADMM) [26], the Adaptive Outlier Pursuit (AOP) method [41], the Penalty Decomposition Algorithm (PDA) [42], L0TV-PADMM [43]
Because of the combination of high-order total variation (TV) regularizer term, which increases the computational complexity of the algorithm, HTVL1 and HOCTVL1 models consume more time compared to TVL1 and CTVL1 model
Summary
In the field of electronics and information, signal processing is a hot research topic, and as a special signal, the study of image has attracted the attention of scholars all over the world [1,2,3]. It is clear that compared with salt-and-pepper noise, the random-valued noise is more difficult to remove since it can be arbitrary number in dmin ≤ f i,j ≤ dmax. For image-restoration problem contaminated by impulse noise, the widely used model is composed of data fidelity term measured by norm and the TV regularization term, which is called TVL1 model [18,19,20]. Numerical experiments show that the two-phase method is superior to TVL1 model, it can handle as high as 90% salt-and-pepper noise, and as high as 55% random-valued noise, while it cannot perform effectively when the level of random-valued noise is higher than 55%. In [31], Gang Liu combined the TV regularizer and the high-order TV regularizer term, and proposed HTVL1 model, which can better remove the impulse noise contrast to TVL1 model.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
