Adaptive Filters for 2-D and 3-D Digital Images Processing

Práce se zabývá adaptivními filtry pro vizualizaci obrazů s vysokým rozlišením. V teoretické části je popsán princip činnosti konfokálního mikroskopu a matematicky korektně zaveden pojem digitální obraz. Pro zpracování obrazů je volen jak frekvenční přístup (s využitím 2-D a 3-D diskrétní Fourierovy transformace a frekvenčních filtrů), tak přístup pomocí digitální geometrie (s využitím adaptivní ekvalizace histogramu s adaptivním okolím). Dále jsou popsány potřebné úpravy pro práci s neideálními obrazy obsahujícími aditivní a impulzní šum. Závěr práce se věnuje prostorové rekonstrukci objektů na základě jejich optických řezů. Veškeré postupy a algoritmy jsou i prakticky zpracovány v softwaru, který byl vyvinut v rámci této práce.The thesis is concerned with filters for visualization of high dynamic range images. In the theoretical part, the principle of confocal microscopy is described and the term digital image is defined in a mathematically correct way. Both frequency approach (using 2-D and 3-D discrete Fourier transform and frequency filters) and digital geometry approach (using adaptive histogram equalization with adaptive neighbourhood) are chosen for the processing of images. Necessary adjustments when working with non-ideal images containing additive and impulse noise are described as well. The last part of the thesis is interested in 3-D reconstruction from optical cuts of an object. All the procedures and algorithms are also implemented in the software developed as a part of this thesis.

Complimentary Image Processing Techniques: Critical Review with C#

Image Enhancement is one of the most essential and laborious techniques in image researches. The scheme of image enhancement is to improve the visual semblance of an image, or to afford a “correct transform representation for future automated image processing. Many images like medical images, satellite images, aerial images and even real life photographs suffer from indigent contrast and noise. It is necessary to enhance the contrast and remove the noise to enhance image quality. One of the most significant stages in medical images detection and analysis is Image Enhancement techniques which improves the quality (clearness) of images for human look, removing blurring and noise, increasing contrast, and unveil details are examples of enhancement operations. The enhancement technique varies from one field to another according to its objective. The existent techniques of image enhancement can be classified into two categories: Spatial Domain and Frequency domain enhancement. In this research, we present an overview of image enhancement projection techniques in spatial domain. More specifically, we categorise processing methods based typical techniques of Image enhancement. Thus the contribution of this paper is to arrange and review image enhancement procedure techniques, attempt an evaluation of shortcomings and universal needs in this field of active research and in last we will stage out promising directions on research for image enhancement for prospective research. Keywords: Frequency based domain enhancement, Image Enhancement, Spatial based domain enhancement, Histogram Equalization

Review on Efficient Contrast Enhancement Technique for Low Illumination Color Images

A digital color image, as its fundamental purpose requires, is to provide a perception of the scene to a human viewer or a computer for carrying out automation tasks such as object recognition. An image of high quality that could truly represent the captured object and the scene is hence in great demand.Contrast is an important factor in any subjective evaluation of image quality. It is the difference in visual properties that makes an object distinguishable from other object and background. On the contrary, the human visual perception is interested in hue (H), saturation (S) and intensity (I) attributes that are carried by the color image. Therefore, when the image has to be processed, most approaches convert the RGB space into some convenient working signal spaces that are close to human perceptions

06221 Abstracts Collection -- Computational Aestethics in Graphics, Visualization and Imaging

From 28.05.06 to 02.06.06, the Dagstuhl Seminar 06221 ``Computational Aesthetics in Graphics, Visualization and Imaging\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Adaptive smoothness constraint image multilevel fuzzy enhancement algorithm

For the problems of poor enhancement effect and long time consuming of the traditional algorithm, an adaptive smoothness constraint image multilevel fuzzy enhancement algorithm based on secondary color-to-grayscale conversion is proposed. By using fuzzy set theory and generalized fuzzy set theory, a new linear generalized fuzzy operator transformation is carried out to obtain a new linear generalized fuzzy operator. By using linear generalized membership transformation and inverse transformation, secondary color-to-grayscale conversion of adaptive smoothness constraint image is performed. Combined with generalized fuzzy operator, the region contrast fuzzy enhancement of adaptive smoothness constraint image is realized, and image multilevel fuzzy enhancement is realized. Experimental results show that the fuzzy degree of the image is reduced by the improved algorithm, and the clarity of the adaptive smoothness constraint image is improved effectively. The time consuming is short, and it has some advantages

Evaluating spatial and frequency domain enhancement techniques on dental images to assist dental implant therapy

Dental imaging provides the patient's anatomical details for the dental implant based on the maxillofacial structure and the two-dimensional geometric projection, helping clinical experts decide whether the implant surgery is suitable for a particular patient. Dental images often suffer from problems associated with random noise and low contrast factors, which need effective preprocessing operations. However, each enhancement technique comes with some advantages and limitations. Therefore, choosing a suitable image enhancement method always a difficult task. In this paper, a universal framework is proposed that integrates the functionality of various enhancement mechanisms so that dentists can select a suitable method of their own choice to improve the quality of dental image for the implant procedure. The proposed framework evaluates the effectiveness of both frequency domain enhancement and spatial domain enhancement techniques on dental images. The selection of the best enhancement method further depends on the output image perceptibility responses, peak signal-to-noise ratio (PSNR), and sharpness. The proposed framework offers a flexible and scalable approach to the dental expert to perform enhancement of a dental image according to visual image features and different enhancement requirements