About: Illumination problem is a research topic. Over the lifetime, 93 publications have been published within this topic receiving 5859 citations.
Papers published on a yearly basis
TL;DR: Some common enhancement methods provided included gray transformation represented by histogram equalization, homomorphic filtering based on illumination-reflectance model, Retinex enhancement theory and gradient field method to solve the non-uniform illumination problem of digital images.
Abstract: In order to solve the non-uniform illumination problem of digital images, which was caused by poor light environment or lighting disproportionation on object’s surface, this paper provided some common enhancement methods.They included gray transformation represented by histogram equalization, homomorphic filtering based on illumination-reflectance model, Retinex enhancement theory and gradient field method.Compared these methods’ effect,analysed their application scopes and gave the solution direction of the non-uniform illumination problem.
••01 Nov 2014
TL;DR: A new method is proposed to solve the problem of non-uniform illumination problem based on double mean filtering by applying a combination between mean and threshold value, the varying background is normalized.
Abstract: In segmentation process, non-uniform illumination problem can affect the segmentation result. In this paper, a new method is proposed to solve the problem based on double mean filtering. By applying a combination between mean and threshold value, the varying background is normalized. This proposed method had been experimented with a few badly illuminated images and the result is evaluated by using Misclassification Error (ME), Sensitivity and Specificity. Based on the ME results, proposed method increases the segmentation correction to 88.27%. Besides that, the sensitivity and specificity of proposed method obtained is 94.56190% and 98.57924% and for classical Otsu is 90.30550% and 61.85435%
TL;DR: In this article, the authors survey the recent advances in the area of illumination conjecture in discrete geometry, computational geometry, and geometric analysis, and describe two new approaches to make progress on the illumination problem.
Abstract: At a first glance, the problem of illuminating the boundary of a convex body by external light sources and the problem of covering a convex body by its smaller positive homothetic copies appear to be quite different. They are in fact two sides of the same coin and give rise to one of the important longstanding open problems in discrete geometry, namely, the Illumination Conjecture. In this paper, we survey the activity in the areas of discrete geometry, computational geometry and geometric analysis motivated by this conjecture. Special care is taken to include the recent advances that are not covered by the existing surveys. We also include some of our recent results related to these problems and describe two new approaches -- one conventional and the other computer-assisted -- to make progress on the illumination problem. Some open problems and conjectures are also presented.
TL;DR: An adaptive single image dehazing algorithm using joint local-global illumination adjustment and the global atmospheric light constant is proposed to be utilized to adaptively compensate the illumination intensity, which may better overcome the dark illumination problem within the dehazed image.
Abstract: Haze has a serious impact on the outdoor optical imaging systems, and it will result in image blurring, color shift, and saturation reduction. Recently, many single image dehazing algorithms have been proposed for practical applications, such as surveillance. However, since the widely-used global atmospheric light in image dehazing fails to well describe the local illumination differences of images, these algorithms fail to well adapt to scenes with different haze concentrations and lighting conditions. Therefore, this paper proposes an adaptive single image dehazing algorithm using joint local-global illumination adjustment. A local illumination estimation for hazy image is proposed to replace the global atmospheric light constant in the atmospheric scattering model, and it can better adapt to the local differences of image illumination. Correspondingly, the global atmospheric light constant is proposed to be utilized to adaptively compensate the illumination intensity, which may better overcome the dark illumination problem within the dehazed image. The experimental results demonstrate that the proposed algorithm can outperform the state-of-the-art algorithms in terms of not only the dehazing effect but also the adaptability.
TL;DR: In this article, the adaptive-network-based fuzzy inference system (ANFIS) is used as the framework for the box-and-ellipse-based ANFIS.
Abstract: In the late 1990s, image processing was first applied to steel bridge coating assessment in the United States. Yet, there has not been any robust method that could solve nonuniform illumination problems to date. In this regard, this paper aims to develop an approach that handles nonuniform illumination in rust image recognition. The proposed approach also contributes to rust intensity recognition and process automation. This paper adopts the a∗ b∗ color configuration (of the L∗ a∗ b∗ color space) which is shown to be the best coordinate system for rust recognition by the proposed method. Different rust colors are regarded as relating to different degrees of rust intensity. Due to the difficulty of defining light rust colors, the adaptive-network-based fuzzy inference system (ANFIS) is used as the framework for the box-and-ellipse-based ANFIS (BE-ANFIS). Illumination adjustment is used to overcome the nonuniform illumination problem. The proposed BE-ANFIS is trained using 120 rust images of the size of 256...
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