Low-light image restoration using discrete cosine transform and conditional generative adversarial network

Abstract

. In the process of low-light imaging, some part of useful information of an image is overwhelmed by a noise. When interference is large, the signal-to-noise ratio (SNR) detected in a system is reduced to a very low level. We study the low-light imaging under condition when the detection SNR is equal to 1 dB. Taking into account that the noise is often located in the high-frequency spectral part, we use discrete cosine transform (DCT) to remove the noise or, at least, filter out its some part. Then we use an algorithm of conditional generative adversarial network (CGAN) to improve the image quality. The simulation results testify that the DCT and CGAN algorithms combined together improve significantly the restoration results and the final quality of images. The latter is high enough, with the average peak SNR being higher than 22 dB and the structural similarity index measure amounting to about 0.8.