We introduce a novel approach for tunable image restoration that achieves the accuracy of multiple models, each optimized for a different level of degradation, with exactly the same number of parameters as a single model. Our model can be optimized to restore as many degradation levels as required with a constant number of parameters and for various image restoration tasks. Experiments on real-world datasets show that our approach achieves state-of-the art results in denoising, DeJPEG and super-resolution with respect to existing tunable models, allowing smoother and more accurate fitting over a wider range of degradation levels.

翻译：我们引入了一种新颖的可金枪鱼图象恢复方法,该方法可以实现多种模型的准确性,每种模型的优化都是为了不同的降解程度,其参数数量与单一模型完全相同。我们的模型可以优化,以恢复尽可能多的降解水平,同时使用固定数量的参数和各种图像恢复任务。 现实世界数据集的实验表明,我们的方法在现有可金枪鱼模型方面达到了最先进的分解结果、DEJPEG和超分辨率,使得更顺畅和更精确地适应范围更广的降解水平。