abstract
- Given its nature of statistical inference, machine learning methods incline to downplay relatively rare events. But in many applications statistical outliers carry disproportional significance; they can, if being left without special treatment as of now, cause CNNs to perform unsatisfactorily on instances of interests. This is the reason why existing CNN image restoration methods all suffer from the problem of blurred details. To overcome this weakness, we advocate a new training methodology to sensitize the CNNs to desired events even they are atypical. Specifically for image restoration, we propose a so-called high frequency feature accentuation space that promotes image sharpness and clarity by maximally discriminating the ground truth image and the CNN-restored image in atypical but semantically important features. Then we force the restored image to agree with the ground truth image in the feature accentuation space by including an auxiliary loss term in the training process. This aims at a high degree of agreement of the two images on high frequency constructs such as sharp edges and fine textures, i.e., penalizes image blurs. The new CNN design method is implemented and tested for tasks of image super-resolution and denoising. Experimental results demonstrate the achievement of our design objective.