Robust and efficient vehicle detection is an important task of environment perception of
intelligent vehicles, which directly affects the behavior decision-making and motion planning …

Advanced automotive active safety systems, in general, and autonomous vehicles, in
particular, rely heavily on visual data to classify and localize objects, such as pedestrians …

Recent progress on Transformers and multi-layer perceptron (MLP) models provide new
network architectural designs for computer vision tasks. Although these models proved to be …

Removing adverse weather conditions like rain, fog, and snow from images is an important
problem in many applications. Most methods proposed in the literature have been designed …

Image restoration tasks demand a complex balance between spatial details and high-level
contextualized information while recovering images. In this paper, we propose a novel …

Underwater images suffer from severe distortion, which degrades the accuracy of object
detection performed in an underwater environment. Existing underwater image …

Existing deep learning based de-raining approaches have resorted to the convolutional
architectures. However, the intrinsic limitations of convolution, including local receptive fields …

Image restoration under adverse weather conditions has been of significant interest for
various computer vision applications. Recent successful methods rely on the current …

Rain streaks in the air appear in various blurring degrees and resolutions due to different
distances from their positions to the camera. Similar rain patterns are visible in a rain image …

Deep learning-based methods have achieved remarkable success in image restoration and
enhancement, but are they still competitive when there is a lack of paired training data? As …