Abstract
Low light imaging usually exhibits defects such as low contrast, low signal-to-noise ratio, low image quality and severe color distortion. Enhancing images taken in such scenes is efficient and rewarding for image restoration and computer vision tasks under demanding imaging conditions. Existing low light image enhancement(LLIE) algorithms rarely take the consistency of image into account, showing insufficiently realistic restoration results. To solve these problem, we propose a LLIE method based on Multi-scale Perception Enhancement of Structural Patch Decomposition and Fusion (MPESPDF-LLIE) to restore images captured in a low light scene. In this model, each image patch is firstly decomposed into four components: perception gain, average intensity, signal strength and signal structure. Next the enhancement model produces the global perception gain and the original enhanced image patches by the inverted dehazing strategy. Based on the original enhanced image patches, an efficient way is used to enhance the signal strength and signal structure in patches. Then a brightness compensation is adopted to improve average intensity. Finally the four components are fused and aggregated to the final image in a multi-scale framework. Experimental results demonstrate that MPESPDF-LLIE is advantageous and effective in preserving detail information and maintaining image consistency, thus MPESPDF-LLIE compensates for the shortcomings of low-light imaging and improves image quality. The source code is available at: https://github.com/LYor61/MPESPDF-LLIE.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call