Presence of haze in images obscures underlying information, which is undesirable in
applications requiring accurate environment information. To recover such an image, a …

Visual degradations caused by motion blur, raindrop, rain, snow, illumination, and fog
deteriorate image quality and, subsequently, the performance of perception algorithms …

Supervised learning-based image dehazing algorithms are sensitive to degradation and
training distribution, making them ill-suited for out-of-domain non-uniform restoration. We …

In this work, we examine the potential application of thermal cameras in improving
perception capabilities in adverse weather conditions like snow, night-time driving, and …

Low-level vision plays a crucial role in a wide range of imaging quality and image
recognition applications. However, the limited size, quality, and diversity of datasets often …

Understanding the driving scene is critical for the safe operation of autonomous vehicles
with state-of-the-art (SoTA) systems relying upon a combination of different algorithms to …

The limited dynamic range of commercial compact camera sensors results in an inaccurate
representation of scenes with varying illumination conditions, adversely affecting image …

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

Increasing usage of traffic cameras provides an opportunity to utilize them for smart city
applications. However, the efficacy of such systems is determined by their ability to detect …

Semantic segmentation algorithms require access to well-annotated datasets captured
under diverse illumination conditions to ensure consistent performance. However, poor …