  We consider the generic deep image enhancement problem where an input image
is transformed into a perceptually better-looking image. Recent methods for
image enhancement consider the problem by performing style transfer and image
restoration. The methods mostly fall into two categories: training data-based
and training data-independent (deep internal learning methods). We perform
image enhancement in the deep internal learning framework. Our Deep Internal
Learning for Image Enhancement framework enhances content features and style
features and uses contextual content loss for preserving image context in the
enhanced image. We show results on both hazy and noisy image enhancement. To
validate the results, we use structure similarity and perceptual error, which
is efficient in measuring the unrealistic deformation present in the images. We
show that the proposed framework outperforms the relevant state-of-the-art
works for image enhancement.
