Dynamic Range Of Gray Levels Research Articles

Adaptive high-gray image enhancement algorithm based on logarithmic mapping and simulated exposure

In the fields of industry, security, military and other areas, high gray-level low contrast images have been widely used. However, due to the large dynamic range of gray level, high noise, blurry detail information and low contrast, it brings difficulties to subsequent image enhancement, target detection and other processes. Ordinary image enhancement algorithms are only suitable for conventional 8-bit images, which are not effective to deal with high-gray and low-contrast images. To address these issues, this paper proposed an adaptive high-gray image enhancement algorithm based on logarithmic mapping and simulated exposure, which was used for processing X-ray film images, infrared images, SAR images and other high gray-level low contrast images. Firstly, a detailed algorithm model construction method was provided for high gray-level low contrast images. Secondly, in order to verify the universality of the algorithm proposed in this paper, qualitative and quantitative experimental analyses were carried out using X-ray film images, infrared images and SAR images respectively. Finally, taking the most complex infrared image in the application environment as an example, the images, enhanced by the algorithm proposed in this paper, were sent to the YOLOv8 neural network for target detection, which improved the detection accuracy. The experiment shows that the algorithm, proposed in this paper, can significantly improve the image quality of high gray-level low contrast images, the detail information is obvious and the contrast is significantly improved. Various indicators are far higher than those of traditional algorithms. At the same time, this algorithm can provide reliable technical support for applications, such as target detection and target tracking in this field.

Developing a Gaussian Model-based Histogram Equalization Technique for Enhancement of Breast Thermography Images

Purpose: Breast cancer is one of the most prevalent diseases among women worldwide. One of the effective ways to reduce the risk of death from breast cancer is early detection by breast screening methods such as thermography. Thermography is non-invasive infrared imaging that detects early symptoms of breast angiogenesis based on the temperature difference and asymmetric patterns between left and right breasts. For better visual perception, it is essential to increase the medical image quality and contrast.
 Materials and Methods: Histogram Equalization (HE) is a common and effective technique for contrast enhancement that uses the whole dynamic range of gray levels. In this paper, we propose to apply the equalization technique to the object part of the image rather than the background. One way is to use Otsu's method for automatic image thresholding. A more efficient approach to extract the body region is to fit a bimodal Gaussian distribution on the temperature information and restrict the equalization on gray level ranges corresponding to temperatures between the mean minus/plus three times of standard deviation.
 Results: We compared the performance of the proposed approach with six conventional HE methods by using objective criteria, including Absolute Mean Brightness Error (AMBE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSI), and Entropy.
 Conclusion: Based on objective measures, as well as subjective visual inspection of the results, the proposed Gaussian model-based HE has better performance in contrast enhancement and brightness preservation among other methods.

An innovative technique for real-time adjusting exposure time of silicon-based camera to get stable gray level images with temperature evolution

Silicon-based sensor cameras are known to be sensitive in the near infrared spectral range, in which a small temperature variation leads to a large modification in the image gray level. It induces acquired images with local saturation or poor dynamic range of gray levels. In order to address this problem, the present study proposes an innovative technique to precisely and automatically adjust the exposure time to obtain stable gray level images when the temperature evolution occurs on the surface of the observed object. Two algorithms, including linear algorithm and Planck’s algorithm, are proposed to predict the exposure time to obtain stable gray level images. Blackbody heating experiment is conducted to validate the accuracy of these two algorithms, and the result indicates that stable gray level images can be obtained using Planck’s algorithm. Moreover, this technique is applied to the specimen heating experiment, and the stable gray level images can also be obtained using Planck’s algorithm. These two experimental results prove that the technique is effective and reliable. Finally, the thermal fields are reconstructed on images of blackbody.

Application of silicon-based camera for measurement of non-homogeneous thermal field on realistic specimen surface

The high-cost low-resolution infrared cameras operating in middle infrared spectral ranges are widely used to detect the thermal fields. In this study, a low-cost high-resolution silicon-based sensor camera operating in near infrared spectral ranges is used to perform the observation of the thermal fields on the realistic steel specimen surface. In near-infrared spectral ranges, a small temperature variation led to a large modification in the sensor illumination, inducing acquired images with over saturation or poor dynamic range of gray levels. To address this problem, an algorithm was used to precisely adjust the exposure time to acquire images with constant gray level whatever the temperature evolution is, and then used in heating experiment of a steel specimen. Results showed that images with constant gray level could be acquired during the heating experiment. A special radiometric model was used to perform near-infrared thermography. Based on this radiometric model of steel specimen surface, the thermal fields on steel specimen surface were successfully reconstructed without measuring surface emissivity.

A Gray-Level Dynamic Range Modification Technique for Image Feature Extraction Using Fuzzy Membership Function

The features of an image must be unique so it is necessary to use certain techniques to ensure them. One of the common techniques is to modify the gray dynamic range of an image. In principle, the gray level dynamic range modification maps the gray level ranges from the input image to the new gray level range as an output image using a specific function. Fuzzy Membership Function (MF) is one kind of membership function that applies the Fuzzy Logic concept. This study uses Trapezoidal MF to map the gray dynamic range of each RGB component to produce a feature of an RGB image. The aim of this study is how to ensure the uniqueness of image features through the setting of Trapezoidal MF parameters to obtain the new dynamic range of gray levels that minimize the possibility of other features other than the selected feature. To test the performance of the proposed method, it also tries to be applied to the signature image. Mean Absolute Error (MAE) calculations between feature labels are performed to test authentication between signatures. The results obtained are for comparison of samples of signature images derived from the same source having a much smaller MAE than the comparison of samples of signature images originating from different sources.

Histogram Equalization Techniques for Contrast Enhancement: A Review

enhancement is one of the widely used techniques for image enhancement. In this technique, contrast of an image becomes better to make the image more acceptable for well human vision. There are several techniques that can be process for contrast enhancement but the most common one is the histogram equalization (HE) for its simplicity. The HE technique remaps gray levels of image according to probability distribution function (PDF). HE spreads the histogram and extends dynamic range of gray levels to accomplish overall contrast enhancement but the drawbacks are excessive change in brightness, excessive contrast enhancement, washed out appearance, loss of naturalness of an image, loss of image details, not displaying the actual appearance of the image so it is not suitable for consumer electronic applications. This paper shows the study of various histogram modifying techniques to overcome these drawbacks in a greater extend.

Object-based multilevel contrast stretching method for image enhancement

The purpose of image enhancement is to improve the perceptibility of information contained in an image. Since the human visual system tends to extract image structure, enhancing the structural features can improve perceived image quality. This paper proposes an object-based multilevel contrast stretching method to enhance image structure. The method first segments the image into its constitute objects, which are treated as image structural components, using morphological watersheds and region merging; then separately stretches the image contrast at interobject level and intra-object level in different ways. At interobject level, an approach of stretching between adjacent local extremes is used to adequately enlarge the local dynamic range of gray levels between objects. At intra-object level, the uniform linear stretching is used to enhance the textural features of objects while maintaining their homogeneity. Since the proposed method directly operates on the object, it can avoid introducing ringing, blocking or other false contouring artifacts in structural appearance; moreover, it can effectively suppress over emphasizing of noise and roughly preserve the overall brightness of the image. Experimental results show that the proposed method can produce enhanced images with more natural appearance in comparison with some classical methods.