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 …

In this paper, we propose an efficient algorithm to directly restore a clear image from a hazy
input. The proposed algorithm hinges on an end-to-end trainable neural network that …

Due to the poor lighting condition and limited dynamic range of digital imaging devices, the
recorded images are often under-/over-exposed and with low contrast. Most of previous …

Due to the spatially variant blur caused by camera shake and object motions under different
scene depths, deblurring images captured from dynamic scenes is challenging. Although …

In modern digital microscopy, deconvolution methods are widely used to eliminate a number
of image defects and increase resolution. In this review, we have divided these methods into …

Multispectral and hyperspectral image fusion (MS/HS fusion) aims to fuse a high-resolution
multispectral (HrMS) and a low-resolution hyperspectral (LrHS) images to generate a high …

Photos taken in hazy weather are usually covered with white masks and often lose important
details. In this paper, we propose a novel deep learning approach for single image dehazing …

Hyperspectral imaging can help better understand the characteristics of different materials,
compared with traditional image systems. However, only high-resolution multispectral …

In this paper, we present an effective and efficient face deblurring algorithm by exploiting
semantic cues via deep convolutional neural networks (CNNs). As face images are highly …

We present a simple and effective approach for non-blind image deblurring, combining
classical techniques and deep learning. In contrast to existing methods that deblur the …