Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …

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

Informative features play a crucial role in the single image super-resolution task. Channel
attention has been demonstrated to be effective for preserving information-rich features in …

We present a new end-to-end generative adversarial network (GAN) for single image motion
deblurring, named DeblurGAN-V2, which considerably boosts state-of-the-art deblurring …

Camera sensors often fail to capture clear images or videos in a poorly lit environment. In
this paper, we propose a trainable hybrid network to enhance the visibility of such degraded …

This paper proposes a human-aware deblurring model that disentangles the motion blur
between foreground (FG) humans and background (BG). The proposed model is based on a …

Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-
scale modeling. However, spatial up-sampling operators (eg, interpolation, transposed …

This paper proposes a novel deep learning approach for single image defocus deblurring
based on inverse kernels. In a defocused image, the blur shapes are similar among pixels …

Single image dehazing, which is the process of removing haze from a single input image, is
an important task in computer vision. This task is extremely challenging because it is …

Non-local interactions play a vital role in boosting performance for image restoration.
However, local window Transformer has been preferred due to its efficiency for processing …