25-07-2012, 02:51 PM
A New Color Filter Array With Optimal Properties for Noiseless and Noisy Color Image Acquisition
72. New Color Filter Array With Optimal Properties.doc (Size: 51 KB / Downloads: 28)
ABSTRACT:
Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA dominates the consumer market, but there has recently been a renewed interest for the design of CFAs [2]–[6]. However, robustness to noise is often neglected in the design, though it is crucial in practice. In this paper, we present a new 2 x 3-periodic CFA which provides, by construction, the optimal tradeoff between robustness to aliasing, chrominance noise and luminance noise. Moreover, a simple and efficient linear demosaicking algorithm is described, which fully exploits the spectral properties of the CFA. Practical experiments confirm the superiority of our design, both in noiseless and noisy scenarios.
EXISTING SYSTEM:
So far, emphasis in CFA design and demosaicking has been put on the minimization of the aliasing artifacts due to spectral overlap of the modulated color channels in the mosaicked image. But with the always increasing resolution of the sensors, aliasing has become a minor issue. In most cases, the optical system is the limiting factor, so that the scene which is sampled by the sensor is bandlimited and moiré artifacts never appear. On the other side, in high-end digital single-lens reflex cameras equipped with expensive and high-quality lenses, an anti-aliasing filter is overlaid on the sensor to get rid of aliasing issues, typically a layer of birefringent material. Still, robustness to aliasing is an important criterion in CFA design, not so much because of potential moiré artifacts, but because it determines the intrinsic resolution of the imaging system.
PROPOSED SYSTEM:
We argue that robustness to noise is more important than robustness to aliasing. High sensitivity properties allow, when acquiring a given picture, to reduce the exposure time (for less blur due to camera shake), to increase the aperture (for increased depth-of-field, hence less out-of-focus blur), or to use a lower ISO setting and a less destructive denoising process. This is particularly important for photography in low light level environments. Hence, there is a real need for new CFAs with improved sensitivity, so that maximum energy of the color scene is packed into the mosaicked image
Software Requirements
• Operating system :- Windows XP Professional
• Front End :- Microsoft Visual Studio .Net 2005
• Coding Language :- C# 2.0
Modules
• Load Image/Save Image
• Image processing techniques
• Color Filters
• HSL Color Space
• Binarization
• Morphology
• Convolution and Correlation
• Edge Detectors
• Histogram
• Gamma Correction filter
Module Description
Load Image/Save Image
Loading the particular image for the image processing, in the particular bitmap. This is by opening the dialog box and selecting the particular image file. After alteration, can save the particular image.
Image processing techniques
Various processing technique are included in the project (invert, grayscale, brightness, contrast, gamma and color).
Color Filters
The color filters are filters placed over the pixel sensors of an image sensor to capture color information. Color filters are needed because the typical photosensors detect light intensity with little or no wavelength specificity, and therefore cannot separate color information. The color filters filter the light by wavelength range, such that the separate filtered intensities include information about the color of light. For example, the Bayer filter gives information about the intensity of light in red, green, and blue (RGB) wavelength regions. The raw image data captured by the image sensor is then converted to a full-color image (with intensities of all three primary colors represented at each pixel) by a demosaicing algorithm which is tailored for each type of color filter. The spectral transmittance of the CFA elements along with the demosaicing algorithm jointly determine the color rendition. The sensor's passband quantum efficiency and span of the CFA's spectral responses are typically wider than the visible spectrum, thus all visible colors can be distinguished. The responses of the filters do not generally correspond to the CIE color matching functions, so a color translation is required to convert the tristimulus values into a common, absolute color space.
HSL Color Space:
HSL and HSV are the two most common cylindrical-coordinate representations of points in an RGB color model, which rearrange the geometry of RGB in an attempt to be more intuitive and perceptually relevant than the cartesian (cube) representation. They are used for color pickers, in color-modification tools in image editing software, and less commonly for image analysis and computer vision.
HSL stands for hue, saturation, and lightness, and is often also called HLS. HSV stands for hue, saturation, and value, and is also often called HSB (B for brightness). A third model, common in computer vision applications, is HSI, for hue, saturation, and intensity. Unfortunately, while typically consistent, these definitions are not standardized, and any of these abbreviations might be used for any of these three or several other related cylindrical models
Binarization:
Image binarization converts an image of up to 256 gray levels to a black and white image. Frequently, binarization is used as a pre-processor before OCR. In fact, most OCR packages on the market work only on bi-level (black & white) images.
The simplest way to use image binarization is to choose a threshold value, and classify all pixels with values above this threshold as white, and all other pixels as black. The problem then is how to select the correct threshold. In many cases, finding one threshold compatible to the entire image is very difficult, and in many cases even impossible. Therefore, adaptive image binarization is needed where an optimal threshold is chosen for each image area.