This paper proposes a new method to improve contrast of a mammogram using multi-energy x-ray (MEX) images. The
x-ray attenuation differences among breast tissues increase as incident photons have lower energy. Thus an image
obtained by a narrow low energy spectrum has higher contrast than a full (wide) energy spectrum image. The proposed
mammogram enhancement utilizes this fact using MEX images. Lowpass data of a low energy spectrum image and high
frequency components of a wide energy spectrum image are combined to have high contrast and low noise.
Nonsubsampled contourlet transform (NSCT) is employed to decompose image data into multi-scale and multidirectional
information. The NSCT overcomes the shortage of directions of wavelet transform by expressing smoothness
along contours sufficiently. The outcome of the transform is a lowpass subband and multiple bandpass directional
subbands. First, the lowpass subband coefficients of a wide energy spectrum image are substituted by those of a low
energy spectrum image. Before the coefficient modification, the low energy spectrum image is processed to have high
contrast and sharp details. Next, for the bandpass directional subbands, the locally adaptive bivariate shrinkage of
contourlet coefficients is applied to suppress noise. The bivariate shrinkage function exploits interscale dependency of
coefficients. Local contrast of the resultant mammogram is considerably enhanced and shows clear fibroglandular tissue
structures. Experimental results illustrate that the proposed method produces a high contrast and low noise level image,
as compared to the conventional mammography based on a single energy spectrum image.
Breast soft tissues have similar x-ray attenuations to mass tissue. Overlapping breast tissue structure often obscures mass
and microcalcification, essential to the early detection of breast cancer. In this paper, we propose new method to generate
the high contrast mammogram with distinctive features of a breast cancer by using multiple images with different x-ray
energy spectra. On the experiments with mammography simulation and real breast tissues, the proposed method has
provided noticeable images with obvious mass structure and microcalifications.
Dynamic range of natural scenes that we see in daily life ranges up to 120dB. Unfortunately, typical imaging devices
only cover about 50dB without any special circuit technique. To overcome this dynamic range problem, many algorithms
and devices have been developed and commercialized. However, commercialization in the field where image quality is
emphasized is not as active as in the filed where the image information is emphasized. This is because there are still
some limitations in capturing and displaying high dynamic range (HDR) image without loss of image information (color,
edge and contrast, etc.) In displaying HDR image, some losses of image information during tone reproduction are
inevitable, since the HDR image have to be processed with some kind of tone reproduction method to compress the
dynamic range fit to the low dynamic range (LDR) display devices. Also there is a report that the tone reproduced LDR
image on LDR display device is viewed better than HDR image on HDR display according to Oh [1]. For this reason, we
propose a new method which can tonally reproduce HDR image into a natural LDR image as auto exposed (AE) one
with minimum loss of image information.
KEYWORDS: Image enhancement, Image processing, RGB color model, Cameras, High dynamic range imaging, Image restoration, Digital imaging, Image sensors, Digital cameras, Linear filtering
In many cases, it is not possible to faithfully capture shadow and highlight image data of a high dynamic range (HDR)
scene using a common digital camera, due to its narrow dynamic range (DR). Conventional solutions tried to solve the
problem with an captured image which has saturated highlight and/or lack of shadow information. In this situation, we
introduce a color image enhancing method with the scene-adaptive exposure control. First, our method recommends an
optimal exposure to obtain more information in highlight by the histogram-based scene analysis. Next, the proposed
luminance and contrast enhancement is performed on the captured image. The main processing consists of luminance
enhancement, multi-band contrast stretching, and color compensation. The luminance and chrominance components of
input RGB data is separated by converting into HSV color space. The luminance is increased using an adaptive log
function. Multi-band contrast stretching functions are applied to each sub-band to enhance shadow and highlight at the
same time. To remove boundary discontinuities between sub-bands, the multi-level low-pass filtering is employed. The
blurred image data represents local illumination while the contrast-stretched details correspond to reflectance of the
scene. The restored luminance image is produced by the combination of multi-band contrast stretched image and multilevel
low-pass filtered image. Color compensation proportional to the amount of luminance enhancement is applied to
make an output image.
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