Guided image filter has been widely used in image processing. Considering the Non-local model is an excellent method for global information accumulation, the non-local image guided filter has been proposed and shown good performance in many image processing tasks by utilizing the non-local similarity of the guidance image. In this paper, we introduce a shadowed non-local image guided filter derived from the concept of shadowed sets. The shadowed non-local model applies more reliable non-local information by suppressing the low similarity values of the guidance image to zero and boosting high similarity values to the maximum of the non-local similarity set. The thresholds of suppression and boosting are determined automatically based on the concept of shadowed sets. Experimental results on several image processing tasks including image denoising, depth super-resolution, and image dehazing demonstrate the superiority of shadowed set based approach.