Multiple exposure fusion for high dynamic range image acquisition

28. Multiple exposure fusion for high dynamic range image acquisition 

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.

Hardware Requirements  & Software Requirements:

Hardware Requirements

•                     SYSTEM                    : Pentium IV 2.4 GHz

•                     HARD DISK              : 40 GB

•                     FLOPPY DRIVE       : 1.44 MB

•                     MONITOR                 : 15 VGA colour

•                     MOUSE                      : Logitech.

•                     RAM                           : 256 MB

•                     KEYBOARD : 110 keys enhanced.

Software Requirements

•                     Operating system        :-  Windows XP Professional

•                     Front End                    :-  JAVA

Modules

• SPATIAL FILTERING:
• Smoothing spatial filters:
• Smoothing Linear Filters:
• Smoothing Non-Linear Spatial filters:
• Sharpening Spatial Filters:

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.

Smoothing Non-Linear Spatial filters:

  

Non-linear Spatial filters are Order-statistics filters whose response is based on ordering(ranking) the pixels contained in the image area encompassed by the filter and then replacing the value of the center pixel with the value determined by the ranking result.  The best known example in this category is the Median filter, which as its name implies replaces the value of a pixel by the median of the gray levels in the neighborhood of that pixel.

Sharpening Spatial Filters:

The principal objective of sharpening is to highlight fine detail in an image or to 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.

REFERENCE:

Takao Jinno, Masahiro Okuda, “Multiple exposure fusion for high dynamic range image acquisition”, IEEE 2011.