24-09-2012, 12:48 PM
UNSHARP MASKING ALGORITHM IN DIGITAL IMAGE PROCESSING
UNSHARP MASKING.docx (Size: 50 KB / Downloads: 39)
Abstract :.
The problem of creating artifact-free upscaled images appearing sharp and natural to the human observer is probably more interesting and less trivial than it may appear. The solution to the problem, often referred to also as ”single image super-resolution”, is related both to the statistical relationship between low resolution and high resolution image sampling and to the human perception of image quality. In many practical applications, simple linear or cubic interpolation algorithms are applied for this task, but the results obtained are not really satisfactory, being affected by relevant artifacts like blurring and jaggies. Several methods have been proposed to obtain better results, involving edge modeling or statistical learning. The most powerful ones, however, present a high computational complexity and are not suitable for real time applications, while fast methods, even if edge-adaptive, are not able to provide artifacts-free images. In this project we describe a new upscaling method based on a two step grid filling and an iterative correction of the interpolated pixels obtained by minimizing an objective function depending on the second order directional derivatives of the image intensity.
Enhancement of contrast and sharpness of an image is required in many applications.unsharp masking is a classical tool for sharpness enhancement.we propose a generalized unsharp masking algorithm using the explotory data model as a unified framework.the proposed algorithm is designed to address two issues:
1.Simultaneously enhancing contrast and sharpness by means of a individual treatment of the model component and the residual.
2.Reducing the halo effect by means of an edge-preserving filter.
We show that the constraints used to derive the function are related with those applied in another well known interpolation method providing good results but computationally heavy. The high quality of the images enlarged with the new method is demonstrated with objective and subjective tests, while the computation time is reduced of 1-2 orders of magnitude with respect to NEDI, so that we were able, using a GPU implementation based on the nVidia CUDA technology, to obtain real time performances.