DILIE Deep Internal Learning for Image Enhancement (WACVW' 2022)

We consider the generic deep image enhancement problem where an input image is transformed into a perceptually better-looking image. The methods mostly fall into two categories: training with prior examples methods and training with no-prior examples methods. Recently, Deep Internal Learning solutions to image enhancement in training with no-prior examples setup are gaining attention. We perform image enhancement using a deep internal learning framework. Our Deep Internal Learning for Image Enhancement framework (DILIE) enhances content features and style features and preserves semantics in the enhanced image. 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 DILIE framework outputs good quality images for hazy and noisy image enhancement tasks.

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