19-07-2012, 02:50 PM
Image Enhancements
SEMINAR REPORT on Image Enhancements.docx (Size: 998.36 KB / Downloads: 41)
INTRODUCTION
Image enhancement techniques are used to emphasize and sharpen image features for display and analysis. Image enhancement is the process of applying these techniques to facilitate the development of a solution to a computer imaging problem. Consequently, the enhancement methods are application specific and are often developed empirically.
The type of techniques includes point operations, where each pixel is modified according to a particular equation that is not dependent on other pixel values; mask operations, where each pixel is modified according to the values of the pixel's neighbors (using convolution masks); or global operations, where all the pixel values in the image (or subimage) are taken into consideration. Spatial domain processing methods include all three types, but frequency domain operations, by nature of the frequency (and sequency) transforms, are global operations. Of course, frequency domain operations can become "mask operations," based only on a local neighborhood, by performing the transform on small image blocks instead of the entire image.
Overall, image enhancement methods are used to make images look better. What works for one application may not be suitable for another application, so the development of enhancement methods require problem domain knowledge, as well as image enhancement expertise. Assessment of the success of an image enhancement algorithm is often "in the eye of the beholder," so image enhancement is as much an art as it is a science.
BASICS OF IMAGE EDITING
Raster images are stored in a computer in the form of a grid of picture elements, or pixels. These pixels contain the image's color and brightness information. Image editors can change the pixels to enhance the image in many ways. The pixels can be changed as a group, or individually, by the sophisticated algorithms within the image editors. The domain of this article primarily refers to bitmap graphics editors, which are often used to alter photographs and other raster graphics. However, vector graphics software, such as Adobe Illustrator, CorelDRAW, Xara Designer Pro or Inkscape, are used to create and modify vector images, which are stored as descriptions of lines, Bézier splines, and text instead of pixels. It is easier to rasterize a vector image than to vectorize a raster image; how to go about vectorizing a raster image is the focus of much research in the field of computer vision. Vector images can be modified more easily, because they contain descriptions of the shapes for easy rearrangement. They are also scalable, being rasterizable at any resolution
Images – they're incredibly versatile, come in a variety of formats, and deliver context, information, and emotions that words alone struggle to convey. Having a clean and balanced image for your website, business, or blog to support your words can also make a huge difference for your site. Kit Heathcock, otherwise known as content creator OriginalOrange, shares the basics of image editing so that you can transform your snapshots and graphics into images that will take your words to the next level.
Newspapers have always used dramatic images to sell their stories. An image is the hook that draws the reader’s eye to read the words. It breaks up the text, brightens the page and sets the mood for the article. Websites use photos in the same way, to catch the reader before they navigate away from a page, to draw them on to reading the content.
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You don’t have to be a professional photographer to produce good enough images for web use. It does help to shoot a good clear image in the first place, but simple snapshots can be improved a lot in an image editing program.
Whether you use a free image editing program like GIMP or Picasa, or purchase software such as Adobe Photoshop, learning just a few basic image editing techniques can turn a photo from dull waste of space to effective hook for your website.
AUTOMATIC IMAGE ENHANCEMENTS
Camera or computer image editing programs often offer basic automatic image enhancement features that correct color hue and brightness imbalances as well as other image editing features, such as red eye removal, sharpness adjustments, zoom features and automatic cropping. These are called automatic because generally they happen without user interaction or are offered with one click of a button or mouse button or by selecting an option from a menu. Additionally, some automatic editing features offer a combination of editing actions with little or no user interaction.
We describe an automatic image enhancement technique based on features extraction methods. The approach takes into account images in Bayer data format, captured using a CCD/CMOS sensor and/or 24-bit color images; after identifying the visually significant features, the algorithm adjusts the exposure level using a “camera response”-like function; then a final HUE reconstruction is achieved. This method is suitable for handset devices acquisition systems (e.g., mobile phones, PDA, etc.). The process is also suitable to solve some of the typical drawbacks due to several factors such as poor optics, absence of flashgun, and so forth.
Image enhancement, image analysis, image sharpening, feature extraction We propose a measure for image sharpness, which facilitates automatic image sharpness enhancement. This way blurry images will be sharpened more whereas sufficiently sharp images will not be sharpened at all. The measure employs localized frequency content analysis in a feature-based context. Thereby it avoids many of the pitfalls of alternative methods: Frequency domain methods provide excellent sharpness measures for images of similar scenes, however they fail when the scene changes. Feature-based methods concentrate on features, however assumptions required for good performance are too restrictive for general purposes. The proposed sharpness measure correlates well with perceived sharpness, and is to a large degree invariant to image content. Furthermore, we show that the proposed image sharpness measure can be used to drive an enhancement algorithm, which will sharpen an input image to a nominal measure. Last but not least, the proposed sharpness measure is computationally efficient, and requires fewer computations than a 3x3 convolution.
DIGITAL DATA COMPRESSION
Many image file formats use data compression to reduce file size and save storage space. Digital compression of images may take place in the camera, or can be done in the computer with the image editor. When images are stored in JPEG format, compression has already taken place. Both cameras and computer programs allow the user to set the level of compression.
Some compression algorithms, such as those used in PNG file format, are lossless, which means no information is lost when the file is saved. By contrast, the JPEG file format uses alossy compression algorithm by which the greater the compression, the more information is lost, ultimately reducing image quality or detail that can not be restored. JPEG uses knowledge of the way the human brain and eyes perceive color to make this loss of detail less noticeable.
In computer science and information theory, data compression, source coding, or bit-rate reduction involves encoding information using fewer bits than the original representation. Compression can be either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by identifying marginally important information and removing it.
Compression is useful because it helps reduce the consumption of resources such as data space or transmission capacity. Because compressed data must be decompressed to be used, this extra processing imposes computational or other costs through decompression. For instance, a compression scheme for video may require expensive hardware for the video to be decompressed fast enough to be viewed as it is being decompressed, and the option to decompress the video in full before watching it may be inconvenient or require additional storage. The design of data compression schemes involve trade-offs among various factors, including the degree of compression, the amount of distortion introduced (e.g., when using lossy data compression), and the computational resources required to compress and uncompress the data.