Real-Time Exposure Image Enhancement Algorithm Based on Curve Estimation
Addressing the issue of reduced image quality due to exposure errors,image enhancement aims to im-prove the quality of regions affected by exposure errors without compromising the quality of normally exposed are-as.However,in recent research,it is uncommon to find algorithms that address multiple exposure issues simulta-neously,and such algorithms often require a significant number of parameters and memory,inevitably increasing costs and time consumption.This paper introduces a novel algorithm that efficiently enhances image quality using a lightweight Transformer network with global-local perception and a global-local luminance enhancement curve,designed to operate effectively within the limited resources of edge devices.The lightweight network con-sists of two main components:a global branch and a local branch.The global branch utilizes Transformer modules to extract the most suitable global parameter mapping,distinguishing and adjusting the global information of the image.The local branch acquires pixel information from the image to estimate the optimal local parameter map-ping.Finally,the iterative application of a global-local luminance enhancement curve containing global and local parameter mappings enhances image quality.Experimental results on an exposure error dataset indicate that the proposed algorithm achieves image quality comparable to current state-of-the-art algorithms with only 5％of the parameters and 0.1％of the floating-point operations,significantly improving efficiency.
NETL
NSTL
万方数据
维普
