Fast and Adaptive Boosting Techniques for Variational Based Image Restoration

Variational based problems are an important class of problems and have a space of improvement in image processing. Boosting techniques have been shown capable of improving many image restoration algorithms. This paper considers four fast and adaptive boosting techniques for variational based image restoration. The adaptive boosting frameworks can compute the existing image restoration algorithm iteratively. The primary idea is to get an enhanced result by using the output of the current step as a part of the input for the next step. Our techniques can boost variational based regularization models like total variation (TV) and total generalized variation (TGV). For image restoration, we used an adaptive regularization parameter selection, which produces signals with more details and preserves tiny objects. For efficient numerical optimization, we implement the alternating direction method of multipliers (ADMM) and demonstrate the effectiveness of the proposed techniques with a variety of experimental results. The simulation results show that the proposed boosting techniques achieve a better restoration performance on comparisons with TV and TGV in terms of quality metrics such as signal to noise ratio (SNR) and structure similarity (SSIM).