Imaging in scattering media has been a challenging and important subject in optical
science. In scattering media, the image quality is often severely degraded by the scattering …

Many computer vision techniques infer properties of our physical world from images.
Although images are formed through the physics of light and mechanics, computer vision …

Single image dehazing is a challenging ill-posed problem which estimates latent haze-free
images from observed hazy images. Some existing deep learning based methods are …

We present an effective and efficient method that explores the properties of Transformers in
the frequency domain for high-quality image deblurring. Our method is motivated by the …

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

Vision Transformers have shown promising performance in image restoration, which usually
conduct window-or channel-based attention to avoid intensive computations. Although the …

Existing deep learning methods for image deblurring typically train models using pairs of
sharp images and their blurred counterparts. However, synthetically blurring images does …

We propose an effective image dehazing algorithm which explores useful information from
the input hazy image itself as the guidance for the haze removal. The proposed algorithm …

The recent proliferation of fake portrait videos poses direct threats on society, law, and
privacy [1]. Believing the fake video of a politician, distributing fake pornographic content of …

In this paper, we present an extensive review on deep learning methods for image
restoration tasks. Deep learning techniques, led by convolutional neural networks, have …