Performance Analysis of Conditional GANs based Image-to-Image Translation Models for Low-Light Image Enhancement

With the evolution of generative adversarial networks, popularly known as GANs for image-to-image translations, conditional GANs (cGANs) are explored and employed for various digital image preprocessing (enhancement and de-noising) tasks. The series of tasks includes image processing such as image enhancement, de-hazing, de-noising, resolution enhancement, and many more. In image enhancement, the area of increasing light (brightness) in low-light images (or poorly-illuminated images) is investigated in this work. For low-light image enhancement, the performance of pix2pix and pix2pixHD models has been demonstrated and analyzed. An analysis of low-light image enhancement using pix2pix model with other loss functions is also presented. Furthermore, pix2pix performance with instance normalization layers for low-light image enhancement is studied, and improved full-reference Image Quality Assessment (FIQA) metrics values along with entropy (a no-reference IQA (NIQA) metric) are reported. The quantitative and qualitative results are also compared with selected cutting-edge deep learning frameworks for low-light image enhancement. In this research, it is found that pix2pix model enhancement metrics are better than RetinexNet model. And the pix2pixHD results are comparable to the latest low-light image enhancement deep learning frameworks such as MIRNet and LLFlow. Furthermore, pix2pix models are lighter in size than MIRNet. The inference times achieved using pix2pix are the minimum on both the CPU and the GPU.