Histogram equalization and optimal profile compression based approach for colour image enhancement

The histogram equalization process is a simple yet efficient image contrast enhancement technique that generally produces satisfactory results. However, due to its design limitations, output images often experience a loss of fine details or contain unwanted viewing artefacts. One reason for such imperfection is a failure of some techniques to fully utilize the allowable intensity range in conveying the information captured from a scene. The proposed colour image enhancement technique introduced in this work aims at maximizing the information content within an image, whilst minimizing the presence of viewing artefacts and loss of details. This is achieved by weighting the input image and the interim equalized image recursively until the allowed intensity range is maximally covered. The proper weighting factor is optimally determined using the efficient golden section search algorithm. Experiments had been conducted on a large number of images captured under natural indoor and outdoor environment. Results s...

Image contrast enhancement using histogram equalization with maximum intensity coverage

Detail enhancement for high-dynamic-range infrared images based on guided image filter

Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from over-enhancement of the background, noise amplification, and brightness distortion. To cope with these problems, an infrared image enhancement method based on adaptive histogram partition and brightness correction is proposed. First, the grayscale histogram is adaptively segmented into several sub-histograms by a locally weighted scatter plot smoothing algorithm and local minima examination. Then, the fore-and background sub-histograms are distinguished according to a proposed metric called grayscale density. The foreground sub-histograms are equalized using a local contrast weighted distribution for the purpose of enhancing the local details, while the background sub-histograms maintain the corresponding proportions of the whole dynamic range in order to avoid over-enhancement. Meanwhile, a visual correction factor considering the property of human vision is designed to reduce the effect of noise during the procedure of grayscale re-mapping. Lastly, particle swarm optimization is used to correct the mean brightness of the output by virtue of a reference image. Both qualitative and quantitative evaluations implemented on real infrared images demonstrate the superiority of our method when compared with other conventional methods.

Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

A fully customized enhancement scheme for controlling brightness error and contrast in magnetic resonance images

Dynamic range reduction and detail enhancement are two important issues for effectively displaying high-dynamic-range images acquired by thermal camera systems. They must be performed in such a way that the high dynamic range image signal output from sensors is compressed in a pleasing manner for display on lower dynamic range monitors without reducing the perceptibility of small details. In this paper, a new method of display and detail enhancement for high dynamic range infrared images is presented. This method effectively maps the raw acquired infrared image to 8-bit domain based on the same architecture of bilateral filter and dynamic range partitioning approach. It includes three main steps: First, a bilateral filter is applied to separate the input image into the base component and detail component. Second, refine the base and detail layer using an adaptive Gaussian filter to avoid unwanted artifacts. Then the base layer is projected to the display range and the detail layer is enhanced using an adaptive gain control approach. Finally, the two parts are recombined and quantized to 8-bit domain. The strength of the proposed method lies in its ability to avoid unwanted artifacts and adaptability in different scenarios. Its great performance is validated by the experimental results tested with two real infrared imagers.

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Display and detail enhancement for high-dynamic-range infrared images

Thresholding segmentation method is the commonly applied technique for extracting objects from the background of the image. If the single object in the image is clearly distinguishable from the background, the histogram of the image would be bimodal and the threshold can be easily chosen at the bottom of the histogram valley. However, histograms are not always bimodal. Thus, new methods are required to solve this problem. This paper raises a novel contrast enhancement method called range limited double-threshold multi-histogram equalization (RLDTMHE). First, we deduce the Otsu’s double-thresholds method, and divide the image into three parts with the double thresholds. In order to preserve the brightness, range of the equalized image is calculated to yield minimum absolute mean brightness error (AMBE) between the output image and the original one. Finally, each sub-image is equalized independently using the new range. Experiment results show that our algorithm obtains more clear details, while keeping the brightness of the original image very well.

Range limited double-thresholds multi-histogram equalization for image contrast enhancement

There are certain limitations in the application of uncooled focal plane array (FPA) detectors owing to the shortage of the response model that transforms bias voltage to analog output voltage at a certain constant radiation power; Moreover, bias voltage affects the input radiation gain. In this study, we established a response model of a microbolometer through examining the detection theory of a microbolometer and the heat balance equation under the condition of the pulse voltage bias. In the establishment process, we simplified the heat balance equation in order to acquire a simple answer. The experimental data show that, in the full variable range of bias voltages, the biggest difference between the model data and the experiment data is about 0.7 K. This model can reflect the real responses of microbolometers with only small differences, which are acceptable in engineering applications.

Response model of resistance-type microbolometer

This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm (M-AM-SGRPIA) for a class of single input single output (SISO) linear output error models with multi-threshold quantized observations. It proves the convergence of the designed algorithm. A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output (SIMO) or SISO nonlinear systems, and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system. The system input design is accomplished using the measurement technology of random repeatability test, and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable. A modified auxiliary model stochastic gradient recursive algorithm (M-AM-SGRA) is designed to identify the model parameters, and the contraction mapping principle proves its convergence. Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm. 

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

The spatial fixed-pattern noise (FPN) compromises severely the quality of the acquired imagery, even makes such images inappropriate for some applications. In order to lower the FPN, some critical nonuniformity correction (NUC) algorithms such as NUC based on linear model, scene-based NUC and so on have been developed. But each algorithm has some drawbacks: restricted application in small dynamic range of objects temperature, low performance under the drift of the environment temperature and complex calculations. In these cases, we develop a bivariate and quadratic model of the FPA and the NUC technique based on the model. The proposed method does not need any assumptions and is a good solution for hardware implementation. It overcomes the drawbacks of the critical algorithm mentioned above. The last simulations and experiments show that the proposed algorithm exhibits a superior correction effect in both large objects temperature range and environment temperature range.

Ning Liu

Papers

Display and detail enhancement for high-dynamic-range infrared images

Range limited double-thresholds multi-histogram equalization for image contrast enhancement

Response model of resistance-type microbolometer

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

Nonuniformity correction of infrared images based on bivariate quadratic model