EXAGGERATED LEARNING FOR CLEAN-AND-SHARP IMAGE RESTORATION

Deep learning has become a methodology of choice for image restoration tasks, including denoising, super-resolution, deblurring, exposure correction, etc., because of its superiority to traditional methods in reconstruction quality. However, the published deep learning methods still have not solve the old dilemma between low noise level and detail sharpness. We propose a new CNN design strategy, called exaggerated deep learning, to reconcile two mutually conflicting objectives: noise free and detail sharpness. The idea is to deliberately overshoot for the desired attributes in the CNN optimization objective function; the cleanness or sharpness is overemphasized according to different semantic contexts. The exaggerated learning approach is experimented on the restoration tasks of super-resolution and low light correction. Its effectiveness and advantages have been empirically affirmed.