Resolution enhancement of digital images for the mobile terminal

Image super-resolution (SR), the process that improves the resolution, has been used in many real world applications. SR is the preprocessing phase of majority of these applications. The improvement in image resolution improves the performance of image analysis process. The SR of digital images take the low resolution images as inputs. In this article, a learning based digital image SR approach is proposed. The proposed approach uses Convolutional Neural Network (CNN) with leaky rectified linear unit (ReLU) for learning and generalization. The experiments with the test dataset from USC-SIPI indicate that the proposed approach increases the quality of the images in terms of the quantitative metric peak signal to noise ratio. Further, it avoided the problem of dying ReLU.

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Off-axis electron holography applied to the study of interfaces

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100121624 ◽

1992 ◽

Vol 50 (1) ◽

pp. 244-245

Author(s):

J.K. Weiss ◽

M. Gajdardziska-Josifovska ◽

M. R. McCartney ◽

David J. Smith

Keyword(s):

Electric Fields ◽

Resolution Enhancement ◽

Interfacial Structure ◽

Atomic Scale ◽

Bulk Property ◽

Electron Holography ◽

Specimen Thickness ◽

Composition And Structure ◽

Chemical Segregation ◽

Diffraction Effects

Interfacial structure is a controlling parameter in the behavior of many materials. Electron microscopy methods are widely used for characterizing such features as interface abruptness and chemical segregation at interfaces. The problem for high resolution microscopy is to establish optimum imaging conditions for extracting this information. We have found that off-axis electron holography can provide useful information for the study of interfaces that is not easily obtained by other techniques.Electron holography permits the recovery of both the amplitude and the phase of the image wave. Recent studies have applied the information obtained from electron holograms to characterizing magnetic and electric fields in materials and also to atomic-scale resolution enhancement. The phase of an electron wave passing through a specimen is shifted by an amount which is proportional to the product of the specimen thickness and the projected electrostatic potential (ignoring magnetic fields and diffraction effects). If atomic-scale variations are ignored, the potential in the specimen is described by the mean inner potential, a bulk property sensitive to both composition and structure. For the study of interfaces, the specimen thickness is assumed to be approximately constant across the interface, so that the phase of the image wave will give a picture of mean inner potential across the interface.

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