Digital still cameras generally use an optical low-pass filter(OLPF) to enhance the image quality by removing high spatial frequencies causing aliasing. While eliminating the OLPF can save manufacturing costs, images captured without using an OLPF include moiré in the high spatial frequency region of the image. Therefore, to reduce the presence of moiré in a captured image, this paper presents a moiré reduction method without the use of an OLPF. First, the spatial frequency response(SFR) of the camera is analyzed and moiré regions detected using patterns related to the SFR of the camera. Using these detected regions, the moiré components represented by the inflection point between the high frequency and DC components in the frequency domain are selected and then removed. Experimental results confirm that the proposed method can achieve moiré reduction while preserving detail information.
KEYWORDS: Reflectivity, Cameras, Imaging systems, Optical filters, Error analysis, Data acquisition, Sensors, RGB color model, Data analysis, Digital cameras
To accurately represent the colors in a real scene, a multi-channel camera system is necessary. One of the applications of
the data acquired with the multi-channel camera system is the spectral reflectance estimation. One of the most widely
used methods to estimate the spectral reflectance is the Wiener estimation. While simple and accurate in controlled
conditions, the Wiener estimation does not perform as well with real scene data. Therefore, the adaptive Wiener
estimation has been proposed to improve the performance of the Wiener estimation. The adaptive Wiener estimation
uses a similar training set that was adaptively constructed from the standard training set according to the camera
responses. In this paper, a new way of constructing such similar training set using the correlation between each spectral
reflectance in the standard training set and the first approximation of the spectral reflectance that was obtained by the
Wiener estimation is proposed. The experimental results showed that the proposed method is more accurate than the
conventional Wiener estimations.
The spatial gamut-mapping algorithm (SGMA) overcomes the drawbacks of the widely used color-by-color methods.
Spatial gamut mapping can preserve detailed information in original images by performing adaptive gamut mapping in
surrounding pixels within the image. However, spatial gamut mapping can result in hue shift and the halo effect. In
addition, it only preserves the boundary information outside the color gamut; the resulting gamut-mapped image does not
sufficiently preserve the detailed information in the input image. In this paper, we propose an SGMA that utilizes details
of the input image. Our approach improves detail that is not effectively represented with conventional spatial gamut
mapping. This is done by taking an original image and first implementing gamut mapping of the input image. Then, the
details of the input image and gamut-mapped image are extracted. By examining the out-of-gamut region, the details of
the input image can be preserved when these values are added to the gamut-mapped image. The resulting image is
obtained by clipping out-of-gamut pixels, since these pixels are generated in the process of preserving details. We
demonstrated that images obtained using the proposed method are more similar to the input images, compared to images
obtained using conventional methods.
KEYWORDS: High dynamic range imaging, Image processing, Cameras, Image analysis, RGB color model, Protactinium, Digital imaging, Digital cameras, Control systems, Light
High dynamic range(HDR) imaging is a technique to represent the wider range of luminance from the lightest and
darkest area of an image than normal digital imaging techniques. These techniques merge multiple images, called as
LDR(low dynamic range) or SDR(standard dynamic range) images which have proper luminance with different exposure
steps, to cover the entire dynamic range of real scenes. In the initial techniques, a series of acquisition process for LDR
images according to exposure steps are required. However, several acquisition process of LDR images induce ghost
artifact for HDR images due to moving objects. Recent researches have tried to reduce the number of LDR images with
optimal exposure steps to eliminate the ghost artifacts. Nevertheless, they still require more than three times of
acquisition processes, resulting ghosting artifacts. In this paper, we propose an HDR imaging from a single Bayer image
with arbitrary exposures without additional acquisition processes. This method first generates new LDR images which
are corresponding to each average luminance from user choices, based on Exposure LUTs(look-up tables). Since the
LUTs contains relationship between uniform-gray patches and their average luminances according to whole exposure
steps in a camera, new exposure steps for any average luminance can be easily estimated by applying average luminance
of camera-output image and corresponding exposure step to LUTs. Then, objective LDR images are generated with new
exposure steps from the current input image. Additionally, we compensate the color generation of saturated area by
considering different sensitivity of each RGB channel from neighbor pixels in the Bayer image. Resulting HDR images
are then merged by general method using captured images and estimated images for comparison. Observer's preference
test shows that HDR images from the proposed method provides similar appearance with the result images using
captured images.
KEYWORDS: Image enhancement, RGB color model, Distortion, Visibility, Image processing, Color reproduction, Digital imaging, High dynamic range imaging, Visual system, Associative arrays
Recently, tone reproduction is widely used in the field of image enhancement and HDR imaging. This method is
especially used to provide the proper luminance so that captured images give the same sensation as the scene. As a result,
we can get high contrast and naturalness of colors. There is ample literature on the topic of tone reproduction that has the
objective of reproducing natural looking color in digital images. In recent papers, IMSR (Integrated multi-scale Retinex)
shows great naturalness in the result images. Most methods, including IMSR, work in RGB or quasi-RGB color spaces,
although some method adopted the use of luminance. This raises hue distortion from the point of the human visual
system, that is, hue distortion in CIELAB color space. Accordingly, this paper proposes an enhanced IMSR method in a
device-independent color space, CIELAB, to preserve hue and obtain high contrast and naturalness. In order to achieve
the devised objectives, a captured sRGB image is transformed to the CIELAB color space. IMSR is then applied to only
L* values, thus the balance of colors components are preserved. This process causes unnatural saturation, therefore
saturation adjustment is performed by applying the ratio of chroma variation at the sRGB gamut boundary according to
the corrected luminance. Finally, the adjusted CIELAB values are transformed to sRGB using the inverse transform
function. In the result images of the proposed method, containing both high and low luminance regions, visibility in dark
shadow and bright regions was improved and color distortion was reduced.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.