Image Signal Processors (ISPs) convert raw sensor signals into digital images, which
significantly influence the image quality and the performance of downstream computer …

Huge advancements have been made over the years in terms of modern image-sensing
hardware and visual computing algorithms (eg, computer vision, image processing, and …

The design of deep network architectures and training methods in computer vision has been
well-explored. However in almost all cases the images have been used as provided with …

Consumer-grade cameras capture the RAW physical description of a scene and then
process the image signals to obtain high-quality RGB images that are faithful to human …

We present a two-stage mechanism for generic image restoration in natural driving
conditions, where multiple non-linear degradations simultaneously impact perception for …

The rapid progress in intelligent vehicle technology has led to a significant reliance on
computer vision and deep neural networks (DNNs) to improve road safety and driving …

Using RAW-images in computer vision problems is surprisingly underexplored considering
that converting from RAW to RGB does not introduce any new capture information. In this …

ISP (Image Signal Processor) serves as a pipeline converting unprocessed raw images to
sRGB images, positioned before nearly all visual tasks. Due to the varying spectral …

Low-light conditions have an adverse impact on machine cognition, limiting the performance
of computer vision systems in real life. Since low-light data is limited and difficult to annotate …

Current deep learning approaches in computer vision primarily focus on RGB data
sacrificing information. In contrast, RAW images offer richer representation, which is crucial …