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 …