Sparse Support Regression For Image Superresolution

• doi: 10.1109/JPHOT.2015.2484287
• title: Sparse Support Regression for Image Super-Resolution
• publisher: IEEE
• isbn:
• issn: 1943-0647
• rank: 3614
• access_type: OPEN_ACCESS
• content_type: Journals
• abstract: In most optical imaging systems and applications, images with high resolution (HR) are desired and often required. However, charged coupled device (CCD) and complementary metal-oxide semiconductor (CMOS) sensors may be not suitable for some imaging applications due to the current resolution level and consumer price. To transcend these limitations, in this paper, we present a novel single image super-resolution method. To simultaneously improve the resolution and perceptual image quality, we present a practical solution that combines manifold learning and sparse representation theory. The main contributions of this paper are twofold. First, a mapping function from low-resolution (LR) patches to HR patches will be learned by a local regression algorithm called sparse support regression, which can be constructed from the support bases of LR-HR dictionary. Second, we propose to preserve the geometrical structure of image patch dictionary, which is critical for reducing artifacts and obtaining better visual quality. Experimental results demonstrate that the proposed method produces high-quality results, both quantitatively and perceptually.
• article_number: 7283533
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7283533
• html_url: https://ieeexplore.ieee.org/document/7283533/
• abstract_url: https://ieeexplore.ieee.org/document/7283533/
• publication_title: IEEE Photonics Journal
• conference_location:
• conference_dates:
• publication_number: 4563994
• is_number: 7222813
• publication_year: 2015
• publication_date: Oct. 2015
• start_page: 1
• end_page: 11
• citing_paper_count: 37
• citing_patent_count: 0
• download_count: 1325
• insert_date: 20150929
• index_terms:
  • ieee_terms:
    • Manifolds
    • Image resolution
    • Geometry
    • Image reconstruction
    • Dictionaries
    • Training
    • Encoding
  • author_terms:
    • Optical Imaging System
    • Super-Resolution
    • Manifold Learning
    • Sparse Representation
    • Optical imaging system
    • super-resolution
    • manifold learning
    • sparse representation
  • dynamic_index_terms:
    • Sparse Regression
    • Sparse Support
    • CCD Camera
    • Charge-coupled Device
    • Optical System
    • Optical Devices
    • Geometric Structure
    • Image Patches
    • Sparse Representation
    • Single Image Super-resolution
    • Perceptual Image Quality
    • Root Mean Square Error
    • Root-mean-square Deviation
    • RMSD
    • RMSE
    • High-resolution Images
    • Regularization Parameter
    • Geodesic
    • Low-resolution Images
    • Back Projection
    • Local Geometry
    • Locally Linear Embedding
    • Nonlinear Dimensionality Reduction
    • Smooth Manifold
    • Bicubic Interpolation
    • Sparse Coding
    • Sample Patches
    • Geometry Of Space
    • Nearest Neighbor Graph
    • Super-resolution Reconstruction
    • Average Pixel Value
    • Texture Regions
    • Patch Pairs
• authors:
  • Author Name: Junjun Jiang
    Affiliation: School of Computer Science, China University of Geosciences, Wuhan, China
    Author URL: https://ieeexplore.ieee.org/author/37898149400
    ID: 37898149400
    Order: 1
    Author Affiliations:
    • School of Computer Science, China University of Geosciences, Wuhan, China
  • Author Name: Xiang Ma
    Affiliation: Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China
    Author URL: https://ieeexplore.ieee.org/author/37085493704
    ID: 37085493704
    Order: 2
    Author Affiliations:
    • Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China
  • Author Name: Zhihua Cai
    Affiliation: School of Information Engineering, Chang'an University, Xi'an, China
    Author URL: https://ieeexplore.ieee.org/author/37272968800
    ID: 37272968800
    Order: 3
    Author Affiliations:
    • School of Information Engineering, Chang'an University, Xi'an, China
  • Author Name: Ruimin Hu
    Affiliation: School of Computer, Wuhan University, Wuhan, China
    Author URL: https://ieeexplore.ieee.org/author/37287163400
    ID: 37287163400
    Order: 4
    Author Affiliations:
    • School of Computer, Wuhan University, Wuhan, China

Image Sensor

• sensor_type: CMOS
• resolution: Not specified
• dynamic_range: Not specified
• pixel_size: Not specified
• dark_current: Not specified

Optical Data

• focal_length: Not specified
• aperture: Not specified
• field_of_view: Not specified
• distortion: Not specified

Performance Metrics

Applications & Benefits

• cell_imaging: Not specified
• benefits: Enables digital imaging in a variety of applications such as smartphones, medical devices, and automotive systems.

Supporting Organizations

• supported_by: Not specified

Manuscript Details

• publication_date: Oct. 2015

Relevancy Score

• score: 8
• missing_fields:
  • resolution
  • dynamic_range
  • pixel_size
  • dark_current
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • frame_rate
  • sensitivity
  • shutter_speed
  • power_consumption
  • noise
  • cell_imaging
  • supported_by

Processed JSON Filename

request_273d0dba-e39f-4207-ab2c-19a0b751b27f-sparse_support_regression_for_image_superresolution.json