Adaptive Context-Aware Generative Adversarial Network for Low-quality Image Enhancement - Xingyu Pan; Fengling Chen (Journal of Applied Science and Engineering)
Low-quality image enhancement methods can effectively improve image quality and details, which have attracted great attention in various fields. However, current methods still face with two issues: (1) They commonly earn a deterministic generation mapping between low-quality and normal images via relying on pixel-level reconstruction, leading to improper brightness and noise in the enhancing process. (2) They use only one type of generative model, either explicit or implicit, which limits flexibility and efficiency of models. To this end, a novel flow-based generative adversarial network with dual attention (FGAN-DA) is devised for data generation. Specifically, FGAN-DA constructs a hybrid generative model via combining explicit and implicit components within the GAN architecture, which effectively alleviates detail blurred and singularity caused by sole generation modeling. FGAN-DA comprises the dual attention feature extraction, invertible flow generation network, the Markov discriminant network. The three modules seamlessly collaborate in enhancing images with good perceptual quality, which effectively boosts the performance of FGAN-DA. Finally, quantitative metrics and visual quality evaluations demonstrate that FGAN-DA sets a new baseline in can generate images with good perceptual quality.