N. Yashwanth Kumar, P. Veerendhar, Dk. Sai Kumar Reddy, Mr. Kundan. B 02 08 Low light image improvement is a grueling task that has attracted considerable attention filmland taken in low light conditions frequently have bad visual quality To address the problem we regard the low light improvement as a residual literacy problem thats to estimate the residual between low and normal light images In this paper we propose a new Deep Lightening Network DLN that benefits from the recent development of Convolutional Neural Networks CNNs The proposed DLN consists of several Lightening Back protuberance LBP blocks The LBPs perform lightening and darkening processes iteratively to learn the residual for normal light estimations To effectively use the original and global features we also propose a point Aggregation FA block that adaptively fuses the results of different LBPs We estimate the proposed system on different datasets Numerical results show that our proposed DLN approach outperforms other styles under both objective and private criteria Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026