23-06-2012, 01:44 PM
Multiple exposure fusion for high dynamic range image acquisition
[attachment=25245]
ABSTRACT:
A multiple exposure fusion to enhance the dynamic range of an image is proposed. The
construction of high dynamic range images (HDRI) is performed by combining multiple
images taken with different exposures and estimating the irradiance value for each pixel.
This is a common process for HDRI acquisition. During this process, displacements of
the images caused by object movements often yield motion blur and ghosting artifacts.
To address the problem, this paper presents an efficient and accurate multiple exposure
fusion technique for the HDRI acquisition. Our method estimates displacements,
occlusion and saturated regions simultaneously by using MAP(Maximum a Posteriori)
estimation, and constructs motion blur free HDRIs. We also propose a new weighting
scheme for the multiple image fusion. We demonstrate that our HDRI acquisition
algorithm is accurate even for images with large motion.
Existing System:
· The field of Digital Image Processing refers to processing digital images by
means of digital computer. One of the main application areas in Digital Image
Processing methods is to improve the pictorial information for human
interpretation.
· Most of the digital images contain noise. This can be removed by many
enhancement techniques.
Proposed System:
· Smoothing filters are used for blurring and for noise reduction.
· Blurring is used in preprocessing steps, such as removal of small details from an
image prior to object extraction and bridging of small gaps in lines or curves.
· Noise reduction can be accomplished by blurring with a linear filter and also by
linear and also by non linear filtering.
· The principal objective of sharpening is to highlight fine detail in image or
enhance detail that has been blurred, either in error or as a natural effect of a
particular method of image acquisition.
· Uses of image sharpening vary and include applications ranging from electronic
printing and medical imaging to industrial inspection and autonomous guidance in
military systems.
Module Description
SPATIAL FILTERING:
Filtering operations that are performed directly on the pixels of an image, are referred as
Spatial Filtering.The process of spatial filtering consists simply of moving the filter mask
from point to point in an image. At each point(x, y), the response of the filter at that
point is calculated using a predefined relationship. For linear spatial filtering the
response is given by a sum of products of the filter coefficients and the corresponding
image pixels in the area spanned by the filter mask.
Smoothing spatial filters:
Smoothing filters are used for blurring and for noise reduction. Blurring is used in
preprocessing steps, such as removal of small details from an image prior to object
extraction, and bridging of small gaps in lines or curves. Noise reduction can be
accomplished by blurring with a linear filter and also by non-linear filtering.
Smoothing Linear Filters:
The response of smoothing, linear spatial filter is simply the average of the pixels
contained in the neighborhood of the filter mask. These filters sometimes are called
averaging filters. They are also referred to as low pass filters.
[attachment=25245]
ABSTRACT:
A multiple exposure fusion to enhance the dynamic range of an image is proposed. The
construction of high dynamic range images (HDRI) is performed by combining multiple
images taken with different exposures and estimating the irradiance value for each pixel.
This is a common process for HDRI acquisition. During this process, displacements of
the images caused by object movements often yield motion blur and ghosting artifacts.
To address the problem, this paper presents an efficient and accurate multiple exposure
fusion technique for the HDRI acquisition. Our method estimates displacements,
occlusion and saturated regions simultaneously by using MAP(Maximum a Posteriori)
estimation, and constructs motion blur free HDRIs. We also propose a new weighting
scheme for the multiple image fusion. We demonstrate that our HDRI acquisition
algorithm is accurate even for images with large motion.
Existing System:
· The field of Digital Image Processing refers to processing digital images by
means of digital computer. One of the main application areas in Digital Image
Processing methods is to improve the pictorial information for human
interpretation.
· Most of the digital images contain noise. This can be removed by many
enhancement techniques.
Proposed System:
· Smoothing filters are used for blurring and for noise reduction.
· Blurring is used in preprocessing steps, such as removal of small details from an
image prior to object extraction and bridging of small gaps in lines or curves.
· Noise reduction can be accomplished by blurring with a linear filter and also by
linear and also by non linear filtering.
· The principal objective of sharpening is to highlight fine detail in image or
enhance detail that has been blurred, either in error or as a natural effect of a
particular method of image acquisition.
· Uses of image sharpening vary and include applications ranging from electronic
printing and medical imaging to industrial inspection and autonomous guidance in
military systems.
Module Description
SPATIAL FILTERING:
Filtering operations that are performed directly on the pixels of an image, are referred as
Spatial Filtering.The process of spatial filtering consists simply of moving the filter mask
from point to point in an image. At each point(x, y), the response of the filter at that
point is calculated using a predefined relationship. For linear spatial filtering the
response is given by a sum of products of the filter coefficients and the corresponding
image pixels in the area spanned by the filter mask.
Smoothing spatial filters:
Smoothing filters are used for blurring and for noise reduction. Blurring is used in
preprocessing steps, such as removal of small details from an image prior to object
extraction, and bridging of small gaps in lines or curves. Noise reduction can be
accomplished by blurring with a linear filter and also by non-linear filtering.
Smoothing Linear Filters:
The response of smoothing, linear spatial filter is simply the average of the pixels
contained in the neighborhood of the filter mask. These filters sometimes are called
averaging filters. They are also referred to as low pass filters.