On the blind denoising efficiency of image denoising algorithms through robustness, image quality and computational burden

The main goal of the image denoising is to recover the original image while attaining the structure of the image as much as possible. When the image denoising task is blind, we have no a priori information about the original image. Thus, we cannot measure the degradation level in the image directly; instead, noise variance can be estimated by the denoising algorithm. According to the estimated value, denoising is performed. Such algorithms are supposed to be robust to varying and high levels of noise interference. Moreover, in time-constrained real-world applications, they must balance the tradeoff between image quality and computation time. In this study, we assess the performance of the image denoising algorithms armored for these goals. We are aimed to determine the optimal performance yielded by such algorithms and the noise bounds wherein each algorithm is superior. After the experimental work, important conclusions are drawn.