Blind Image Sharpness Assessment And Enhancement via Deep Auxiliary Learning
Qingbo Wu, Rui Ma, King Ngi Ngan, Hongliang Li, Fanman Meng · 2019
In this paper, we propose an unified deep auxiliary learning network to train the blind image sharpness assessment (BISA) metric and enhancer simultaneously. Instead of using the BISA as a parameter tuner like existing works, the proposed method aims to exploit the complementary information between two tasks and boost both of their performance. On the one hand, the enhancement subnetwork tries to separate a blurry image into the clear version and disparity map, which provide additional mask effect and blurry degree information for accurate BISA. On the other hand, the BISA subnetwork help determine the enhancement degree by feeding sharpness-aware features to the enhancer, which is helpful for avoiding under-/over-enhancing. Experimental results on three publicly available databases show that the proposed method outperforms many state-of-the-art algorithms in both the BISA and sharpness enhancement tasks.