Topic
Histogram equalization
About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.
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14 Nov 2005
TL;DR: A wavelet-based normalization method so as to normalize illuminations and enhances the contrast as well as the edges of face images simultaneously, in the frequency domain using the wavelet transform, to facilitate face recognition tasks.
Abstract: The appearance of a face image is severely affected by illumination conditions that hinder the automatic face recognition process. To recognize faces under varying illuminations, we propose a wavelet-based normalization method so as to normalize illuminations. This method enhances the contrast as well as the edges of face images simultaneously, in the frequency domain using the wavelet transform, to facilitate face recognition tasks. It outperforms the conventional illumination normalization method - the histogram equalization that only enhances image pixel gray-level contrast in the spatial domain. With this method, our face recognition system works effectively under a wide range of illumination conditions. The experimental results obtained by testing on the Yale face database B demonstrate the effectiveness of our method with 15.65% improvement, on average, in the face recognition system.
194 citations
12 Dec 2008
TL;DR: The distinct features ofCUDA GPU are analyzed, the general program mode of CUDA is summarized and several classical image processing algorithms by CUDA, such as histogram equalization, removing clouds, edge detection and DCT encode and decode are implemented.
Abstract: CUDA (compute unified device architecture) is a novel technology of general-purpose computing on the GPU, which makes users develop general GPU (graphics processing unit) programs easily. This paper analyzes the distinct features of CUDA GPU, summarizes the general program mode of CUDA. Furthermore, we implement several classical image processing algorithms by CUDA, such as histogram equalization, removing clouds, edge detection and DCT encode and decode etc., especially introduce the first two algorithms. If we donpsilat take the data transfer time in experiment between host memory and device memory into account, as the image size increase, histogram computation can get a more than 40x speedup, removing clouds can get an about 79x speedup, DCT can gain around 8x and edge detection more than 200x.
194 citations
TL;DR: In this paper, an adaptive approach to face recognition is presented to overcome the adverse effects of varying lighting conditions, which is measured in terms of luminance distortion in comparison to a known reference image, will be used as the base for adapting the application of global and region illumination normalization procedures.
Abstract: The accuracy of automated face recognition systems is greatly affected by intraclass variations between enrollment and identification stages. In particular, changes in lighting conditions is a major contributor to these variations. Common approaches to address the effects of varying lighting conditions include preprocessing face images to normalize intraclass variations and the use of illumination invariant face descriptors. Histogram equalization is a widely used technique in face recognition to normalize variations in illumination. However, normalizing well-lit face images could lead to a decrease in recognition accuracy. The multiresolution property of wavelet transforms is used in face recognition to extract facial feature descriptors at different scales and frequencies. The high-frequency wavelet subbands have shown to provide illumination-invariant face descriptors. However, the approximation wavelet subbands have shown to be a better feature representation for well-lit face images. Fusion of match scores from low- and high-frequency-based face representations have shown to improve recognition accuracy under varying lighting conditions. However, the selection of fusion parameters for different lighting conditions remains unsolved. Motivated by these observations, this paper presents adaptive approaches to face recognition to overcome the adverse effects of varying lighting conditions. Image quality, which is measured in terms of luminance distortion in comparison to a known reference image, will be used as the base for adapting the application of global and region illumination normalization procedures. Image quality is also used to adaptively select fusion parameters for wavelet-based multistream face recognition.
193 citations
TL;DR: Experimental results showed that this method makes natural looking images especially when the dynamic range of input image is high and it has been shown by simulation results that the proposed genetic method had better results than related ones in terms of contrast and detail enhancement.
192 citations
TL;DR: The experimental results show that the proposed dynamic histogram specification (DHS) algorithm not only keeps the original histogram shape features but also enhances the contrast effectively.
Abstract: A novel contrast enhancement algorithm is proposed The proposed approach enhances the contrast without losing the original histogram characteristics, which is based on the histogram specification technique It is expected to eliminate the annoying side effects effectively by using the differential information from the input histogram The experimental results show that the proposed dynamic histogram specification (DHS) algorithm not only keeps the original histogram shape features but also enhances the contrast effectively Moreover, the DHS algorithm can be applied by simple hardware and processed in real-time system due to its simplicity
192 citations