Joint super resolution and denoising from a single depth image
This paper describes a new algorithm for depth image super resolution and denoising using
a single depth image as input. A robust coupled dictionary learning method with locality …
a single depth image as input. A robust coupled dictionary learning method with locality …
Single depth image super resolution and denoising via coupled dictionary learning with local constraints and shock filtering
Recently, consumer depth cameras have gained significant popularity due to their affordable
cost. However, the limited resolution and quality of the depth map generated by these …
cost. However, the limited resolution and quality of the depth map generated by these …
Self-learning-based post-processing for image/video deblocking via sparse representation
Blocking artifact, characterized by visually noticeable changes in pixel values along block
boundaries, is a common problem in block-based image/video compression, especially at …
boundaries, is a common problem in block-based image/video compression, especially at …
Adaptive image super-resolution algorithm based on fractional Fourier transform
Super-resolution imaging is a critical image processing stage that improves visual image
quality. Super-resolution imaging has a wide array of use in different fields, such as medical …
quality. Super-resolution imaging has a wide array of use in different fields, such as medical …
Single image super‐resolution based on sparse representation using dictionaries trained with input image patches
R Asgarian Dehkordi, H Khosravi… - IET Image …, 2020 - Wiley Online Library
In this study, an efficient self‐learning method for image super‐resolution (SR) is presented.
In the proposed algorithm, the input image is divided into equal size patches. Using these …
In the proposed algorithm, the input image is divided into equal size patches. Using these …
Fast single image SR via dictionary learning
In this study, the authors propose a fast method for single image super‐resolution (SR). The
relation between high‐resolution (HR)/low‐resolution (LR) patches is learned using the …
relation between high‐resolution (HR)/low‐resolution (LR) patches is learned using the …
Image and video restoration and enhancement via sparse representation
The chapter provides a survey of recent advances in image/video restoration and
enhancement via spare representation. Images/videos usually unavoidably suffer from …
enhancement via spare representation. Images/videos usually unavoidably suffer from …
Learning-based leaf image recognition frameworks
Automatic plant identification via computer vision techniques has been greatly important for
a number of professionals, such as environmental protectors, land managers, and foresters …
a number of professionals, such as environmental protectors, land managers, and foresters …
An adaptive single image method for super resolution
In this paper we propose an adaptive method for single image super resolution by exploiting
the self-similarity. By using similarity between patches of input image and a down sampled …
the self-similarity. By using similarity between patches of input image and a down sampled …
Single depth map super-resolution: A self-structured sparsity representation with non-local total variation technique
Recently, depth maps introduce a very effective representation for solving many
fundamental computer vision problems. However, modern 3D scanning devices, such as …
fundamental computer vision problems. However, modern 3D scanning devices, such as …