IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network

Soumya Shubhra Ghosh, Yang Hua, Sankha Subhra Mukherjee, Neil Robertson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
278 Downloads (Pure)


Despite the breakthroughs in quality of image enhancement, an end-to-end solution for simultaneous recovery of the finer texture details and sharpness for degraded images with low resolution is still unsolved. Some existing approaches focus on minimizing the pixel-wise reconstruction error which results in a high peak signal-to-noise ratio. The enhanced images fail to provide high-frequency details and are perceptually unsatisfying, i.e., they fail to match the quality expected in a photo-realistic image. In this paper, we present Image Enhancement Generative Adversarial Network (IEGAN), a versatile framework capable of inferring photo-realistic natural images for both artifact removal and super-resolution simultaneously. Moreover, we propose a new loss function consisting of a combination of reconstruction loss, feature loss and an edge loss counterpart. The feature loss helps to push the output image to the natural image manifold and the edge loss preserves the sharpness of the output image. The reconstruction loss provides low-level semantic information to the generator regarding the quality of the generated images compared to the original. Our approach has been experimentally proven to recover photo-realistic textures from heavily compressed low-resolution images on public benchmarks and our proposed high-resolution World100 dataset.
Original languageEnglish
Title of host publicationWACV 2019: The IEEE Winter Conference on Applications of Computer Vision
Number of pages10
Publication statusPublished - 07 Mar 2019

Publication series

NameIEEE Winter Conference on Applications of Computer Vision (WACV): Proceedings
ISSN (Print)1550-5790


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