Histogram equalization

About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.

Histogram equalization method and device in contrast enhancement apparatus for image processing system

Abstract: In contrast enhancement during image processing, a histogram equalization process or apparatus divides an equalization image screen into overlapping windows and performs histogram equalization on each window. Using windows improves contrast of pixel values that are rare throughout the entire image and reduces or prevents loss of information represented by the rare pixel values. Overlapping the windows reduces discontinuities at the boundaries of the windows. The size and overlap of the windows can be adjusted according to the time available for equalization and can be optimized according to the desired image quality improvement. The histogram equalization can be selectively performed on an entire image screen or any part thereof to reduce image degradation in the entire or part of an image. As a further aspect of the invention, to reduce the number of operations required for equalization, a histogram for a current window is obtained starting from the histogram for a previous window. To further improve image quality, a histogram equalization apparatus uses low-pass- or band-pass-filtered pixel data as a lookup table address to output a histogram-equalized value. This helps remove amplified thermal or quantization noise.

A Tone-Mapping Technique Based on Histogram Using a Sensitivity Model of the Human Visual System

Abstract: High-dynamic-range (HDR) images require tone mapping to be displayed properly on lower dynamic range devices. In this paper, a tone-mapping algorithm that uses histogram of luminance to construct a lookup table (LUT) for tone mapping is presented. Characteristics of the human visual system (HVS) are used to give more importance to visually distinguishable intensities while constructing the histogram bins. The method begins with constructing a histogram of the luminance channel, using bins that are perceived to be uniformly spaced by the HVS. Next, a refinement step is used, which removes the pixels from the bins that are indistinguishable by the HVS. Finally, the available display levels are distributed among the bins proportionate to the pixels counts thus giving due consideration to the visual contribution of each bin in the image. Quality assessment using both quantitative evaluations and user studies suggests that the presented algorithm produces tone-mapped images that are visually pleasant and preserve details of the original image better than the existing methods. Finally, implementation details of the algorithm on GPU for parallel processing are presented, which could achieve a significant gain in speed over CPU-based implementation.

Histogram Specification: A Fast and Flexible Method to Process Digital Images

Abstract: Histogram specification has been successfully used in digital image processing over the years. Mainly used as an image enhancement technique, methods such as histogram equalization (HE) can yield good contrast with almost no effort in terms of inputs to the algorithm or the computational time required. More elaborate histograms can take on problems faced by HE at the expense of having to define the final histograms in innovative ways that may require some extra processing time but are nevertheless fast enough to be considered for real-time applications. This paper proposes a new technique for specifying a histogram to enhance the image contrast. To further evidence our faith on histogram specification techniques, we also discuss methods to modify images, e.g., to help segmentation approaches. Thus, as advocates of these techniques, we would like to emphasize the flexibility of this image processing approach to do more than enhancing images.

Cbir using color histogram processing

Abstract: Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. In this scenario, it is necessary to develop appropriate information systems to efficiently manage these collections. The most common approaches use Content-Based Image Retrieval (CBIR). The goal of CBIR systems is to support image retrieval based on content e.g., shape, color, texture. In this paper color extraction and comparison were performed using the three color histograms, conventional color histogram (CCH), invariant color histogram (ICH) and fuzzy color histogram (FCH) .The conventional color histogram (CCH) of an image indicates the frequency of occurrence of every color in an image. The appealing aspect of the CCH is its simplicity and ease of computation. There are however, several difficulties associated with the CCH. The first of these is the high dimensionality of the CCH, even after drastic quantization of the color space. Another downside of the CCH is that it does not take into consideration color similarity across different bins and cannot handle rotation and translation. To address the problem of rotation and translation an invariant color histograms(ICH) based on the color gradients is used and to address the problem of spatial relationship fuzzy linking color histogram (FCH) is used.

A Robust CBIR Approach Using Local Color Histograms

Abstract: Global color histograms are well-known as a simple and often way to perform color-based image retrieval. However, it lacks spatial information about the image colors. The use of a grid of cells superimposed on the images and the use of local color histograms for each such cell improves retrieval in the sense that some notion of color location is taken into account. In such an approach however, retrieval becomes sensitive to image rotation and translation. In this thesis we present a new way to model image similarity, also using colors and a superimposing grid, via bipartite graphs. As a result, the technique is able to take advantage of color location but is not sensitive to rotation and translation. Experimental results have shown the approach to be very effective. If one uses global color histograms as a filter then our approach, named Harbin, becomes quite effcient as well (i.e., it imposes very little overhead over the use of global color histograms).