  The limited dynamic range of commercial compact camera sensors results in an
inaccurate representation of scenes with varying illumination conditions,
adversely affecting image quality and subsequently limiting the performance of
underlying image processing algorithms. Current state-of-the-art (SoTA)
convolutional neural networks (CNN) are developed as post-processing techniques
to independently recover under-/over-exposed images. However, when applied to
images containing real-world degradations such as glare, high-beam, color
bleeding with varying noise intensity, these algorithms amplify the
degradations, further degrading image quality. We propose a lightweight
two-stage image enhancement algorithm sequentially balancing illumination and
noise removal using frequency priors for structural guidance to overcome these
limitations. Furthermore, to ensure realistic image quality, we leverage the
relationship between frequency and spatial domain properties of an image and
propose a Fourier spectrum-based adversarial framework (AFNet) for consistent
image enhancement under varying illumination conditions. While current
formulations of image enhancement are envisioned as post-processing techniques,
we examine if such an algorithm could be extended to integrate the
functionality of the Image Signal Processing (ISP) pipeline within the camera
sensor benefiting from RAW sensor data and lightweight CNN architecture. Based
on quantitative and qualitative evaluations, we also examine the practicality
and effects of image enhancement techniques on the performance of common
perception tasks such as object detection and semantic segmentation in varying
illumination conditions.
