With the goal of recovering high-quality image content from its degraded version, image
restoration enjoys numerous applications, such as in surveillance, computational …

Given a degraded input image, image restoration aims to recover the missing high-quality
image content. Numerous applications demand effective image restoration, eg …

Deep learning methods have witnessed the great progress in image restoration with specific
metrics (eg, PSNR, SSIM). However, the perceptual quality of the restored image is relatively …

Image restoration is a long-standing problem in image processing and low-level computer
vision. Recently, discriminative convolutional neural network (CNN)-based approaches …

Since convolutional neural networks (CNNs) perform well at learning generalizable image
priors from large-scale data, these models have been extensively applied to image …

Image restoration tasks demand a complex balance between spatial details and high-level
contextualized information while recovering images. In this paper, we propose a novel …

While scale-invariant modeling has substantially boosted the performance of visual
recognition tasks, it remains largely under-explored in deep networks based image …

The aim of this paper is to propose a large scale dataset for image restoration (LSDIR).
Recent work in image restoration has focused on the design of deep neural networks. The …

The aim of this paper is to propose a mechanism to efficiently and explicitly model image
hierarchies in the global, regional, and local range for image restoration. To achieve that, we …

Self-similarity refers to the image prior widely used in image restoration algorithms that small
but similar patterns tend to occur at different locations and scales. However, recent …