Joint Enhancement And Denoising of Low Light Images Via JND Transform

Abstract

Low light images suffer from low dynamic range and severe noise due to low signal-to-noise ratio (SNR). In this paper, we propose joint enhancement and denoising of low light images via just-noticeable-difference (JND) transform. We achieve contrast enhancement and noise reduction simultaneously based on human visual perception. First, we perform contrast enhancement based on perceptual histogram to effectively allocate a dynamic range while preventing over-enhancement. Second, we generate JND map based on an HVS response model from foreground and background luminance, called JND transform. Then, we refine JND map using Weber's law and visual masking. Weber's law enhances the JND map based on the luminance variation after enhancement, while visual masking provides noise suppression for smooth regions and detail enhancement for texture regions. Finally, we conduct chroma denoising that transfers texture information of the denoised luma channel to the chroma channels by guided image filtering. Experimental results show that the proposed method achieves good performance in contrast enhancement and noise reduction while successfully preserving details.