Non-identical residual learning for image enhancement via dynamic multi-level perceptual loss

Residual learning based deep generative networks have achieved promising performance in image enhancement.However,due to the large color gap between a low-quality image and its highquality version,the identical mapping in conventional residual learning cannot explore the elaborate detail differences,resulting in color deviations and texture losses in enhanced images.To address this issue,an innovative non-identical residual learning architecture is proposed,which views image enhancement as two complementary branches,namely a holistic color adjustment branch and a finegrained residual generation branch.In the holistic color adjustment,an adjusting map is calculated for each input low-quality image,in order to regulate the low-quality image to the high-quality representation in an overall way.In the fine-grained residual generation branch,a novel attention-aware recursive network is designed to generate residual images.This design can alleviate the overfitting problem by reusing parameters and promoting the network’s adaptability for different input conditions.In addition,a novel dynamic multi-level perceptual loss based on the error feedback ideology is proposed.Consequently,the proposed network can be dynamically optimized by the hybrid perceptual loss provided by a well-trained VGG,so as to improve the perceptual quality of enhanced images in a guided way.Extensive experiments conducted on publicly available datasets demonstrate the state-of-the-art performance of the proposed method.