Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

The advent of deep learning has brought a revolutionary transformation to image denoising
techniques. However, the persistent challenge of acquiring noise-clean pairs for supervised …

Along with the deraining performance improvement of deep networks, their structures and
learning become more and more complicated and diverse, making it difficult to analyze the …

Blind deconvolution is a classical yet challenging low-level vision problem with many real-
world applications. Traditional maximum a posterior (MAP) based methods rely heavily on …

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 …

Retinex theory is developed mainly to decompose an image into the illumination and
reflectance components by analyzing local image derivatives. In this theory, larger …

Recent deep network-based compressive sensing (CS) methods have achieved great
success. However, most of them regard different sampling matrices as different independent …

Deep convolutional neural networks (CNNs) with strong expressive ability have achieved
impressive performances on single image super-resolution (SISR). However, their excessive …

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 …

This paper presents a new variational inference framework for image restoration and a
convolutional neural network (CNN) structure that can solve the restoration problems …