Low-light image enhancement using negative feedback pulse coupled neural network

Abstract

Low-light image enhancement, fundamentally an ill-posed problem, seeks to simultaneously provide superior visual effects and preserve the natural appearance. Current methodologies often exhibit limitations in contrast enhancement, noise reduction, and the mitigation of halo artifacts. Negative feedback pulse coupled neural network (NFPCNN) is proposed to provide a well posed solution based on uniform distribution in contrast enhancement. The negative feedback dynamically adjusts the attenuation amplitude of neuron threshold based on recent neuronal ignited state. Neurons in the concentrated brightness area arrange smaller attenuation amplitude to enhance the local contrast, whereas neurons in the sparse area set larger attenuation amplitude. NFPCNN makes up for the negligence of pulse coupled neural network in the brightness distribution of the input image. Consistent with Weber–Fechner law, gamma correction is employed to adjust the output of NFPCNN. Although contrast enhancement can improve detail expressiveness, it might also introduce artifacts or aggravate noise. To mitigate these issues, the bilateral filter is employed to suppress halo artifacts. Brightness is used as coefficient to refine the Relativity-of-Gaussian noise suppression method. Experimental results show that the proposed method can effectively suppress noise while enhancing image contrast.