Local Contrast Enhancement Algorithm Research Articles

Automatic Detection of Cast Billet Dendrite Based on Improved Hough Transform

Primary dendrite information is one of the most important metrics to measure the quality of continuous cast slabs. The contrast of low magnification images is very low under the influence of illumination and sampling devices, so the traditional dendrite detection method has the problem of missed detections. We propose an automatic dendrite detection method based on an improved Hough transform, which effectively improves the accuracy and efficiency of primary dendrite detection. By using the local grayscale features of the image, a genetic algorithm-based local contrast enhancement algorithm is proposed. Compared with the traditional contrast enhancement algorithm, it can retain all the information of the dendrites. Combined with the image binarization method based on Hessian matrix, we can obtain more detailed information about the dendrites. According to the continuity and solidification characteristics of dendrites, the Hough transform is improved to extract dendrite information, which effectively reduces the computational cost of the Hough transform. The experimental results show that the method of this paper has versatility, and the error is four pixels compared with the manual method, which can provide a reliable basis for the subsequent judgement of the quality of cast billets.

Enhanced Infrared Detection Algorithm for Weak Targets in Complex Backgrounds

In this article, we design a new lightweight infrared optical system that fully meets airborne settings and greatly reduces the collection of invalid information. This new system targets the technical problems of stray light, strong invalid information, weak texture information of small targets, and low intensity of valid information under a complex background, which lead to difficult identification of small targets. Image enhancement of weak, small targets against complex backgrounds has been the key to improving small-target search and tracking technology. For the complex information that is still collected, an improved two-channel image enhancement processing algorithm is proposed: the A-channel adopts an improved nonlinear diffusion method and improved curvature filtering, and the B-channel adopts bootstrap filtering and a local contrast enhancement algorithm. The weak target is then extracted by the algorithm of weighted superposition. The false alarm rate is effectively weakened, and robustness is improved. As a result of the experimental data analysis, the method can effectively extract the weak targets in complex backgrounds, such as artificial backgrounds, surface vegetation, etc., enlarge the target gray value, and reduce Fa by 56%, compared with other advanced methods, while increasing Pd by 17%. The algorithm proposed in this paper is of great significance and value for weak target identification and tracking, and it has been successfully applied to industrial detection, medical detection, and in the military field.

A contrast enhancement framework under uncontrolled environments based on just noticeable difference

Image contrast enhancement refers to an operation of remapping the pixels’ values of an image to emphasize desired information in the image. In this work, we propose a novel pixel-based (local) contrast enhancement algorithm, based on the human visual perception. First, we make an observation that pixels with lower regional contrast should be amplified for the purpose of enhancing the contrast and pixels with higher regional contrast should be suppressed to avoid undesired over-enhancement. To determine the quality of the regional contrast in the image (either lower or higher), a reference image will be created using a proposed global based contrast enhancement method (termed as Mean Brightness Bidirectional Histogram Equalization in the paper) for fast computation reason. To quantify the abovementioned regional contrast, we propose a method based on human visual perception taking Just Noticeable Difference (JND) into account. In short, our proposed algorithm is able to limit the enhancement of well-contrasted regions and enhance the poor contrast regions in an image. Both objective quality and subjective quality experimental results suggested that the proposed algorithm enhances images consistently across images with different dynamic range. We conclude that the proposed algorithm exhibits excellent consistency in producing satisfactory result for different type of images. It is important to note that the algorithm can be directly implemented in color space and not limited only to grayscale. The proposed algorithm can be obtained from the following GitHub link: https://github.com/UTARSL1/CHE.

Target recognition method of green pepper harvesting robot based on manifold ranking

In order to solve the problem of the recognition of green pepper harvesting robot in the near color background, a target recognition method based on manifold ranking is proposed in this paper. After pepper image enhancement by the local contrast enhancement algorithm, super-pixels extracted via energy-driven sampling (SEEDS) is used to construct super-pixel blocks of the enhanced image, the upper, lower, left and right boundary query nodes are used to query the boundary of the super pixel block image respectively. Then the manifold ranking is used to sort the image boundaries to obtain four saliency maps, and the final saliency map is obtained by fusion. Finally, the noise is removed by morphological operations and the contour of green pepper target is obtained to realize the recognition of green pepper in complex environment. Compared with classification and regression tree (CART), conditional random fields (CRF) and threshold, the proposed method can effectively identify the green pepper target, with the recognition accuracy of 83.6% and recall rate of 81.2%. The performance index is obviously better than the other three methods, which can meet the requirements of the actual operation of the harvesting robot.

Modified Local and Global Contrast Enhancement Algorithm for Color Satellite Image

The quality of remotely sensed satellite images depends on the reflected electromagnetic radiation from the earth’s surface features. Lack of consistent and similar amounts of energy reflected by different features from the earth’s surface results in a poor contrast satellite image. Image enhancement is the image processing of improving the quality that the results are more suitable for display or further image analysis. In this paper, we present a detailed model for color image enhancement using the quaternion framework. We introduce a novel quaternionic frequency enhancement algorithm that can combine the color channels and the local and global image processing. The basic idea is to apply the α-rooting image enhancement approach for different image blocks. For this purpose, we split image in moving windows on disjoint blocks. The parameter alfa for every block and the weights for every local and global enhanced image driven through optimization of measure of enhancement (EMEC). Some presented experimental results illustrate the performance of the proposed approach on color satellite images in comparison with the state-of-the-art methods.

Single image veiling glare removal

ABSTRACTPhotographing against a bright light source is difficult since the direct light from the light source causes glare phenomenon in the lens, changing the colour balance and reducing the image contrast within the field of view. To address the effect, we introduce a glare formation model that accounts for the characters of the glare. Based on the model, we propose a single image processing method to remove the glare in the images by decomposing the image into the scene layer and the glare layer. We then adjust the brightness and the colour balance of the scene layer to eliminate the influences of the glare. In order to enhance the contrast decreased by the glare, we use a local standard deviation-based contrast enhancement algorithm to boost the details while avoiding excessive enhancement. Experimental results show that the method removes the majority of the glare and improves the local contrast effectively.

Image Local Contrast Enhancement Algorithm based on Grey Entropy

Image Local Contrast Enhancement Algorithm based on Grey Entropy

Real-time implementation of an adaptive simultaneous dynamic range compression and local contrast enhancement algorithm on a GPU

Dynamic range compression is a fundamental function required in digital video cameras and display monitors to improve the visual appeal of color images. This paper presents a real-time implementation of an algorithm for adaptive dynamic range compression and contrast enhancement on a graphics processing unit (GPU). To achieve this, we first designed an adaptive intensity transfer function to handle the enhancement of standard-dynamic-range (8 bits/channel) images with both low- and high-intensity. The proposed algorithm then combines the intensity transfer function with an existing simultaneous dynamic range compression and local contrast enhancement (SDRCLCE) algorithm for the simultaneous compression of dynamic range and enhancement of local contrast. To reach real-time performance in processing high-resolution color images, the proposed algorithm is implemented using an NVIDIA GeForce GTX 650 Ti GPU based on NVIDIA’s Compute Unified Device Architecture (CUDA) parallel programming running on a 3.33 GHz Intel Core i5-661 CPU. Compared with a LUT-accelerated implementation, the proposed GPU implementation was shown to accelerate the processing of 1024 × 1024 color images by 11.1 times and color images of 4096 × 4096 pixels by 7.5 times, including the cost of memory copy between the host and device.

Algorithms for contrast enhancement of electronic portal images

Abstract An implementation of two new automatized image processing algorithms for contrast enhancement of portal images is presented as suitable tools which facilitate the setup verification and visualization of patients during radiotherapy treatments. In the first algorithm, called Automatic Segmentation and Histogram Stretching (ASHS), the portal image is automatically segmented in two sub-images delimited by the conformed treatment beam: one image consisting of the imaged patient obtained directly from the radiation treatment field, and the second one is composed of the imaged patient outside it. By segmenting the original image, a histogram stretching can be independently performed and improved in both regions. The second algorithm involves a two-step process. In the first step, a Normalization to Local Mean (NLM), an inverse restoration filter is applied by dividing pixel by pixel a portal image by its blurred version. In the second step, named Lineally Combined Local Histogram Equalization (LCLHE), the contrast of the original image is strongly improved by a Local Contrast Enhancement (LCE) algorithm, revealing the anatomical structures of patients. The output image is lineally combined with a portal image of the patient. Finally the output images of the previous algorithms (NLM and LCLHE) are lineally combined, once again, in order to obtain a contrast enhanced image. These two algorithms have been tested on several portal images with great results.

An adaptive dynamic range compression with local contrast enhancement algorithm for real-time color image enhancement

Dynamic range compression has become an important function used in modern digital video cameras to improve visual quality of color images suffered from low dynamic range and poor contrast defects. This study addresses real-time implementation of an adaptive dynamic range compression algorithm for color image/video enhancement. To achieve this purpose, we first propose a new image-dependent nonlinear intensity-transfer function to produce a satisfactory dynamic range compression result with less color artifacts. The proposed algorithm is then derived by combining the new adaptive nonlinear intensity-transfer function with an efficient local contrast enhancement algorithm. Moreover, an algorithmic acceleration method is also presented to accelerate the processing speed of the proposed color image enhancement method, achieving real-time performance in processing high-definition video signals. Experimental results validate the performance of the developed method in terms of quantitative evaluation, visual quality, computational efficiency, and power consumption.

A novel simultaneous dynamic range compression and local contrast enhancement algorithm for digital video cameras

This article addresses the problem of low dynamic range image enhancement for commercial digital cameras. A novel simultaneous dynamic range compression and local contrast enhancement algorithm (SDRCLCE) is presented to resolve this problem in a single-stage procedure. The proposed SDRCLCE algorithm is able to combine with many existent intensity transfer functions, which greatly increases the applicability of the proposed method. An adaptive intensity transfer function is also proposed to combine with SDRCLCE algorithm that provides the capability to adjustably control the level of overall lightness and contrast achieved at the enhanced output. Moreover, the proposed method is amenable to parallel processing implementation that allows us to improve the processing speed of SDRCLCE algorithm. Experimental results show that the performance of the proposed method outperforms three state-of-the-art methods in terms of dynamic range compression and local contrast enhancement.

An Improved Local Equilibrium Contrast Enhancement Algorithm for Infrared Laser Images

An improved local equilibrium contrast enhancement algorithm based self-adaptive contrast enhancement algorithm is proposed for infrared laser images, in which the image pixel value histogram is divided into three parts: background and noise area, targets area, and uninterested area. The targets parts are highlighted, while the background and noise parts and the uninterested parts are restrained. A comprehensive qualitative and quantitative image enhancement performance evaluation is presented to verify the proposed theory and algorithm validity, efficiency and reasonability. The experimental results indicate that the proposed algorithm can greatly improve the global and local contrast for both near infrared images and far infrared laser images while efficiently reducing noise in the infrared laser images,and the visual quality of enhanced image is obviously better than the enhancement of the traditional histogram equalization, double plateaus histogram equalization algorithm, etc.