Local Histogram Equalization Research Articles

Face recognition across illumination

Illumination variation on images of faces is one of the most difficult problems in face recognition systems. The performance of a self-organizing map-based face recognition system is highly degraded when the illumination in test images differs from that of the training images. Illumination normalization is a way to solve this problem. Both global and local image enhancement methods are studied in this article. A local histogram equalization method strongly improves the recognition accuracy of the CMU-PIE face database.

Real-time edge-aware image processing with the bilateral grid

We present a new data structure---the bilateral grid , that enables fast edge-aware image processing. By working in the bilateral grid, algorithms such as bilateral filtering, edge-aware painting, and local histogram equalization become simple manipulations that are both local and independent. We parallelize our algorithms on modern GPUs to achieve real-time frame rates on high-definition video. We demonstrate our method on a variety of applications such as image editing, transfer of photographic look, and contrast enhancement of medical images.

Detection of simulated microcalcifications in fixed mammary tissue: an ROC study of the effect of local versus global histogram equalization

To investigate whether sliding window adaptive histogram equalization (SWAHE) of digital mammograms improves the detection of simulated calcifications, as compared to images normalized by global histogram equalization (GHE). Direct digital mammograms were obtained from mammary tissue phantoms superimposed with different frames. Each frame was divided into forty squares by a wire mesh, and contained granular calcifications randomly positioned in about 50% of the squares. Three radiologists read the mammograms on a display monitor. They classified their confidence in the presence of microcalcifications in each square on a scale of 1 to 5. Images processed with GHE were first read and used as a reference. In a later session, the same images processed with SWAHE were read. The results were compared using ROC methodology. When the total areas AZ were compared, the results were completely equivocal. When comparing the high-specificity partial ROC area AZ,0.2 below false-positive fraction (FPF) 0.20, two of the three observers performed best with the images processed with SWAHE. The difference was not statistically significant. When the reader's confidence threshold in malignancy is set at a high level, increasing the contrast of mammograms with SWAHE may enhance the visibility of microcalcifications without adversely affecting the false-positive rate. When the reader's confidence threshold is set at a low level, the effect of SWAHE is an increase of false positives. Further investigation is needed to confirm the validity of the conclusions.

CMBFHE: a novel contrast enhancement technique based on cascaded multistep binomial filtering histogram equalization

Global and local histogram equalization (HE) proved to be effective techniques for contrast enhancement. Local HE allows to achieve high contrast enhancement rates but its high computational complexity limits its applicability in many resource constrained scenarios. On the contrary, global HE complexity is relatively low but enhancement rate is often unsatisfactory. Recently, an algorithm exploiting a low-pass filter (LPF) mask to obtain a partially overlapped sub-block HE effect (POSHE) has been presented. POSHE allows to produce the high local HE contrast with the simplicity of global HE. In this paper a generalization of POSHE approach exploiting cascaded multistep binomial filtering HE (CMBFHE) in order to get the same LPF mask is proposed. By relying on the same filter mask, contrast enhancement capability of CMBFHE is exactly the same of POSHE. Additionally, results show that a significant speedup with respect to the previous fastest method is achieved because of the efficient implementation of the filtering approach; moreover hardware implementations can be effectively designed. This makes CMBFHE a suitable solution in all those consumer electronics related environments including camcorders, digital cameras and video surveillance where on-the-fly local-like contrast enhancement is required

실시간 윈도우 기반 영상 처리를 위한 병렬 하드웨어 구조의 FPGA 구현

윈도우 기반의 영상처리는 전체 영상처리 분야에 있어서 기본이 되는 분야이다. 이러한 윈도우 기반의 영상처리는 처리해야 할 데이터와 연산이 매우 많은 편이기 때문에 범용 컴퓨터 구조에서 소프트웨어 프로그램을 사용하여 윈도우 기반 영상처리에서 필요로 하는 모든 연산을 실시간으로 수행하기 힘들다. 본 논문에서는 FPGA(Field Programmable Gate Array)를 사용하여 윈도우 기반 영상처리를 실시간으로 수행할 수 있는 병렬 하드웨어 구조를 제안하고자 한다. 또한 제안한 구조를 통해 VHDL(VHSIC Hardware Description Language)을 이용하여 윈도우 기반의 영상처리 중 하나인 동적 문턱치화(dynamic thresholding) 회로와 국부 히스토그램 평활화(local histogram equalization) 회로를 설계하고 FPGA로 해당 회로를 구현할 것이다. 구현된 회로의 성능 측정도 다루어 진다. A window-based image processing is an elementary part of image processing area. Because window-based image processing is computationally intensive and data intensive, it is hard to perform ail of the operations of a window-based image processing in real-time by using a software program on general-purpose computers. This paper proposes a parallel hardware architecture that can perform a window-based image processing in real-time using FPGA(Field Programmable Gate Array). A dynamic threshold circuit and a local histogram equalization circuit of the proposed architecture are designed using VHDL(VHSIC Hardware Description Language) and implemented with an FPGA. The performances of both implementations are measured.

Physical interpretation of polarization-encoded images by color preview

In the framework of Stokes parameters imaging, polarizationencoded images have four channels which makes physical interpretation of such multidimensional structures hard to grasp at once. Furthermore, the information content is intricately combined in the parameters channels which involve the need for a proper tool that allows the analysis and understanding this kind of images. In this paper we address the problem of analyzing polarization-encoded images and explore the potential of this information for classification issues and propose ad hoc color displays as an aid to the interpretation of physical properties content. The color representation schemes introduced hereafter employ a technique that uses novel Poincaré Sphere to color spaces mapping coupled with a segmentation map as an a <ital>priori</ital> information in order to allow, at best, a distribution of the information in the appropriate color space. The segmentation process relies on the fuzzy C-means clustering algorithms family where the used distances were redefined in relation with our images specificities. Local histogram equalization is applied to each class in order to bring out the intra-class's information smooth variations. The proposed methods are applied and validated with Stokes images of biological tissues.

Fingerprint Image Enhancement using Filtering Techniques

Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification and classification. Minutiae are local discontinuities in the fingerprint pattern, mainly terminations and bifurcations. Most of the minutiae detection methods are based on image binarization while some others extract the minutiae directly from gray-scale images. In this work we compare these two approaches and propose two different methods for fingerprint ridge image enhancement. The first one is carried out using local histogram equalization, Wiener filtering, and image binarization. The second method uses a unique anisotropic filter for direct gray-scale enhancement. The results achieved are compared with those obtained through some other methods. Both methods show some improvement in the minutiae detection process in terms of time required and efficiency.

An advanced contrast enhancement using partially overlapped sub-block histogram equalization

An advanced histogram-equalization algorithm for contrast enhancement is presented. Histogram equalization is the most popular algorithm for contrast enhancement due to its effectiveness and simplicity. It can be classified into two branches according to the transformation function used: global or local. Global histogram equalization is simple and fast, but its contrast-enhancement power is relatively low. Local histogram equalization, on the other hand, can enhance overall contrast more effectively, but the complexity of computation required is very high due to its fully overlapped sub-blocks. In this paper, a low-pass filter-type mask is used to get a nonoverlapped sub-block histogram-equalization function to produce the high contrast associated with local histogram equalization but with the simplicity of global histogram equalization. This mask also eliminates the blocking effect of nonoverlapped sub-block histogram-equalization. The low-pass filter-type mask is realized by partially overlapped sub-block histogram-equalization (POSHE). With the proposed method, since the sub-blocks are much less overlapped, the computation overhead is reduced by a factor of about 100 compared to that of local histogram equalization while still achieving high contrast. The proposed algorithm can be used for commercial purposes where high efficiency is required, such as camcorders, closed-circuit cameras, etc.

Image Contrast Enhancement by Constrained Local Histogram Equalization

Histogram equalization is a widely used image contrast enhancement method. While global histogram equalization enhances the contrast of the whole image, local histogram equalization can enhance many image details by taking different transformation of the same gray level at different places in the original image. However, the local histogram equalization process often results in unacceptable modification of the original image appearance. In this paper, a constrained local histogram equalization method is proposed to balance the conflicting requirements: enhancement of the image details and the maintenance of the overall image appearance. Our method uses the variational form of histogram equalization so that a constraint condition, which forces the local gray level transformations to change continuously in the spatial domain, can be introduced into the equalization process. Experimental results of different kinds of images show the effect of our method.

Shape preserving local histogram modification

A novel approach for shape preserving contrast enhancement is presented in this paper. Contrast enhancement is achieved by means of a local histogram equalization algorithm which preserves the level-sets of the image. This basic property is violated by common local schemes, thereby introducing spurious objects and modifying the image information. The scheme is based on equalizing the histogram in all the connected components of the image, which are defined based both on the grey-values and spatial relations between pixels in the image, and following mathematical morphology, constitute the basic objects in the scene. We give examples for both grey-value and color images.

A study and modification of the local histogram equalization algorithm

An analysis of the local histogram equalization algorithm is presented. An adaptation of the algorithm is suggested that involves varying the window size over different regions of the image. This enables each region to be enhanced equally. The algorithm has a parameter, S, to control the amount of stretching required. Some results are presented and analysed.

Image contrast enhancement based on the intensities of edge pixels

Histogram modification can improve the contrast of an image. Histogram equalization has been the most popular histogram modification technique. However, the technique has the tendency to magnify local noise for images with large homogeneous regions. In this paper we suggest a new histogram modification technique which utilizes the intensity distribution of the edge pixels of an image. We first identify the edge pixels of an image. Then the intensity histogram of the edge pixels is constructed. An intensity transformation function is derived from the edge-pixel histogram and then applied to the entire image. In general, this transformation will increase the intensity difference between neighboring homogeneous regions. We also have suggested three tools to measure the performance of contrast-enhancing methods. The three measurements are image contrast value, image information loss value, and local intensity variance value. Our goal for enhancing is to significantly increase an image's contrast value while keeping both the information loss value and the local intensity variance value low. In the experiments, we have compared the performance of the suggested method with that of the ordinary histogram equalization technique and the local area histogram equalization (LAHE) technique using both synthetic and real images. The results were then evaluated by the three tools. The suggested method performed very well both analytically and visually.

Four algorithms for enhancing images with large peaks in their histogram

Abstract Four algorithms that modify the histogram equalization algorithm, and extend its capability to a larger range of histograms, are presented. The algorithms allow the user to balance the amount of stretching of peaks in the histogram against the amount of information lost. The effects of the algorithms on peaks or Gaussions having various means, standard deviations and population sizes are discussed. The results are compared to applying local histogram equalization.

Optimum contrast enhancement using fuzzy sets

The local histogram equalization employed in the contrast enhancement of the image contains two problems: the long processing time and the distortion of the image quality due to the overenhancement. To reduce the processing time, sampling and hardware implementation are considered. On the other hand, to improve the image quality, the adjustment of the size of the reference region, the weighting in the reference region and clipping are considered. A problem in past efforts for image quality improvement, however, is that the size of the reference region and the clipping value are set as fixed values, which is one of the reasons that a sufficient improvement of the image quality has not been achieved. It is expected that optimum values exist for the size of the reference region and the clipping for each image and for each part of the image to realize the best contrast. This paper proposes a method to ameliorate the image distortion by adjusting the size of the reference region and the clipping value by the fuzzy control for each part of the image. The result of processing is shown, and the usefulness of the proposed method is discussed.

Real time infrared image processing

The paper studies the techniques of real time infrared image processing. The enhancement of real time infrared image is mainly discussed. With enhancement of real time infrared image, the authors suggest main methods that are local histogram equalization, pseudo color display, average of multiple frames and accumulation of multiple fields, contour enhancement and restoration coefficient. Empasis is laid on the restoration coefficient method, in this paper. On the basis of describing the model of the method, the conerete project for realizing enhancement of the real time infrared image is put forward, and the practical example is given.

Scintigraphic image contrast-enhancement techniques: global and local area histogram equalization.

This article develops two contrast-modification techniques for the display of scintigraphic images. Based on histogram-modification techniques, histogram equalization, where each level of gray is used to the same extent, gives maximum entropy. The first technique uses the application of histogram equalization in the whole image. To eliminate contrast attenuation small but important portion of the gray scale histogram, local area histogram equalization has been applied to images with differences in intensity. Both techniques were tested using a phantom with known characteristics. The global equalization technique is more suitable to bone scintigraphies, and some well-chosen boundaries improved the difference between two comparable areas. For liver scintigraphies, where intensity is quite equal in every pixel, a local area equalization was chosen that allowed detection of heterogeneous structures. The images resulting from histogram-equalization techniques improve the readability of data, but are often far from usual images and necessitate an apprenticeship for the physician.