Object detection under rainy conditions for autonomous vehicles: A review of state-of-the-art and emerging techniques
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 …
particular, rely heavily on visual data to classify and localize objects, such as pedestrians …
Diffusion Models for Image Restoration and Enhancement--A Comprehensive Survey
Image restoration (IR) has been an indispensable and challenging task in the low-level
vision field, which strives to improve the subjective quality of images distorted by various …
vision field, which strives to improve the subjective quality of images distorted by various …
Maxim: Multi-axis mlp for image processing
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 …
network architectural designs for computer vision tasks. Although these models proved to be …
SuperFusion: A versatile image registration and fusion network with semantic awareness
Image fusion aims to integrate complementary information in source images to synthesize a
fused image comprehensively characterizing the imaging scene. However, existing image …
fused image comprehensively characterizing the imaging scene. However, existing image …
Learning a sparse transformer network for effective image deraining
Transformers-based methods have achieved significant performance in image deraining as
they can model the non-local information which is vital for high-quality image reconstruction …
they can model the non-local information which is vital for high-quality image reconstruction …
Uformer: A general u-shaped transformer for image restoration
In this paper, we present Uformer, an effective and efficient Transformer-based architecture
for image restoration, in which we build a hierarchical encoder-decoder network using the …
for image restoration, in which we build a hierarchical encoder-decoder network using the …
Transweather: Transformer-based restoration of images degraded by adverse weather conditions
JMJ Valanarasu, R Yasarla… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
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 …
problem in many applications. Most methods proposed in the literature have been designed …
Pre-trained image processing transformer
As the computing power of modern hardware is increasing strongly, pre-trained deep
learning models (eg, BERT, GPT-3) learned on large-scale datasets have shown their …
learning models (eg, BERT, GPT-3) learned on large-scale datasets have shown their …
Image de-raining transformer
Existing deep learning based de-raining approaches have resorted to the convolutional
architectures. However, the intrinsic limitations of convolution, including local receptive fields …
architectures. However, the intrinsic limitations of convolution, including local receptive fields …
Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model
In this paper, an ill-posed problem of multiple adverse weather removal is investigated. Our
goal is to train a model with a'unified'architecture and only one set of pretrained weights that …
goal is to train a model with a'unified'architecture and only one set of pretrained weights that …