Local Contrast Enhancement in Digital Images Using a Tunable Modified Hyperbolic Tangent Transformation

Contrast enhancement which aims to increase the contrast of an image with low dynamic range, has been widely studied and exploited. In spite of the great success of many contrast enhancement algorithms, they still have difficulty in achieving both global and local contrast enhancement so that some over-enhancement, under-enhancement or even halo artifacts are often produced in complex images. This paper proposes a simple but efficient contrast enhancement method which may achieve both global and local contrast enhancement and perceptually suppress the above-mentioned problems. A cost function integrating global enhancement with local contrast enhancement is firstly constructed to pose image enhancement as an optimization problem. Then, two key steps are involved in solving for an optimal solution, the just-noticeable difference (JND) model is introduced to perceptually determine maximum local gains and neighbouring gray-level difference for local and global contrast enhancement, respectively, and an adaptive parameter regularization method is invoked to further suppress over-enhancement and halo artifacts. The experimental results on many images both qualitatively and quantitatively demonstrate our algorithm can robustly provide better visual quality in global and local contrast compared to a selection of other well-known state-of-the-art algorithms.

Adaptive histogram equalization (AHE) is a method for adaptive contrast enhancement of digital images. It is an automatic, reproducible method for the simultaneous viewing of contrast within a digital image with a large dynamic range. Recent experiments have shown that in specific cases, there is no significant difference in the ability of AHE and linear intensity windowing to display gray-scale contrast. More recently, a variant of AHE which limits the allowed contrast enhancement of the image has been proposed. This contrast-limited adaptive histogram equalization (CLAHE) produces images in which the noise content of an image is not excessively enhanced, but in which sufficient contrast is provided for the visualization of structures within the image. Images processed with CLAHE have a more natural appearance and facilitate the comparison of different areas of an image. However, the reduced contrast enhancement of CLAHE may hinder the ability of an observer to detect the presence of some significant gray-scale contrast. In this report, a psychophysical observer experiment was performed to determine if there is a significant difference in the ability of AHE and CLAHE to depict gray-scale contrast. Observers were presented with computed tomography (CT) images of the chest processed with AHE and CLAHE. Subtle artificial lesions were introduced into some images. The observers were asked to rate their confidence regarding the presence of the lesions; this rating-scale data was analyzed using receiver operating characteristic (ROC) curve techniques. These ROC curves were compared for significant differences in the observers' performances. In this report, no difference was found in the abilities of AHE and CLAHE to depict contrast information.

In this paper we present a generalized algorithm for unsharp masking of medical images which takes as one of its inputs a high contrast image underwent local adaptive contrast enhancement. Selection of optimal values of the number of histogram bins, processing window size and intensity lower and upper limits in iterative manner is part of applying Contrast Limited Adaptive Histogram Equalization (CLAHE). Experimental results reveal higher quality of the output images both in terms of root mean square contrast and sharpness. Achieved quality, both visually and quantitatively, is compared to that from the Adaptive Histogram Equalization (AHE) algorithm, limited histogram stretching and ordinary histogram equalization which proves its applicability. The algorithm is considered appropriate for processing a number of types of images, such as CT, X-ray, etc.

The contrast enhancement of infrared image is useful and important to the infrared image system. The current techniques of local enhancing exists either over-enhancing or high complexity problems. In this paper, we propose a compositive contrast enhancement algorithm which combines histogram equalization based methods (HEBM) and an multi-scales unsharp masking based methods (UMBM). This proposed algorithm uses HEBM to achieve global contrast enhancement and UMBM to achieve local multi-scales contrast enhancement. First, we review the techniques developed in the literature for contrast enhancement. After then, we introduce the new algorithm in details. The performance of the proposed method is studied on experimental IR data and compared with those yielded by two well established algorithms. The developed algorithm has good performance in global contrast and local contrast enhancement with noise and artifact suppression.

The effect of image processing of digital radiographic data on lesion detectability in clinical images has not been systematically studied. In this experiment, we evaluated a low frequency suppression filter program applied to CT digital radiographic localization images (General Electric Scoutview). Three different filter parameters affecting edge enhancement and local image contrast were applied to a set of digital chest images. A standard observer detection experiment comparing the variously filtered digital images and standard chest radiographs in the detection of lung nodules (11 peripheral lung fields, one superimposed on aortic arch) was performed. Standard chest radiography was more sensitive than the digital chest images, although some improvement was noted with increase in local image contrast and edge enhancement. Both image formats were equally specific. Image processing would be better evaluated using a digital imaging system with better performance parameters.

This paper presents a new method suitable for the contrast enhancement of digital images. The proposed algorithm employs the center-surround architecture of the human visual system (HVS) for the comparison of the central pixel with the mean intensity of its surround. These two values are inputs to a mapping function, which calculates the new intensity value of the central pixel, in a way that increases small local intensity differences while preserving the large ones. The proposed method is applied to three spatial scales by employing different surround sizes. As a result, contrast enhancement is performed in different spatial frequencies of the original image. The proposed method is tested against other similar methods and is found to exhibit better results in the enhancement of low-contrast images, such as medical, aerial or foggy scenes, revealing visual information that otherwise would not be available to the human observer.

A modular approach on an adaptive thresholding method for segmentation of cell regions in bioelectric images with complex lighting environments and background conditions is presented in this paper. Preprocessing steps involve low-pass filtering of the image and local contrast enhancement. This image is then adaptively thresholded which produces a binary image. The binary image consists of cell regions and the edges of a metal electrode that show up as bright spots. A local region based approach is used to distinguish between cell regions and the metal electrode tip that cause bright spots. Regional properties such as area are used to separate the cell regions from the non-cell regions. Special emphasis is given on the detection of twins and triplet cells with the help of watershed transformation, which might have been lost if form-factor alone were to be used as the geometrical descriptor to sepa rate the cell and the non-cell regions. Keywords: bioelectric images, image segmentation, adaptive thresholding, cell segmentation, local contrast enhancement, watershed transformation, template matching

This paper proposes a novel algorithm, which enhances the contrast of an input image using spatial information of pixels. The algorithm introduces a new method to compute the spatial entropy of pixels using spatial distribution of pixel gray levels. Different than the conventional methods, this algorithm considers the distribution of spatial locations of gray levels of an image instead of gray-level distribution or joint statistics computed from the gray levels of an image. For each gray level, the corresponding spatial distribution is computed using a histogram of spatial locations of all pixels with the same gray level. Entropy measures are calculated from the spatial distributions of gray levels of an image to create a distribution function, which is further mapped to a uniform distribution function to achieve the final contrast enhancement. The method achieves contrast improvement in the case of low-contrast images; however, it does not alter the image if the image’s contrast is high enough. Thus, it always produces visually pleasing results without distortions. Furthermore, this method is combined with transform domain coefficient weighting to achieve both local and global contrast enhancement at the same time. The level of the local contrast enhancement can be controlled. Several experiments on effects of contrast enhancement are performed. Experimental results show that the proposed algorithms produce better or comparable enhanced images than several state-of-the-art algorithms.

Image Enhancement works as a first mandatory criteria for an efficient image analysis task. Removing noises and managing the contrast are the two major tasks that need to be accomplished in an image enhancement process. In this article, an innovative approach for color image enhancement is proposed. The proposed approach is a two-step technique. The first step is the noise removal step. Here, an improved median filter, Improved_Median(), is introduced to smooth the noises which exist in the original color image. Then, in the second step, local contrast enhancement is done. For that, an improved version of CLAHE, AA_CLAHE() is proposed for the local contrast management of the filtered image. The V-channel of HSV color space is used for the color computations involved in the local contrast management process. The overall enhancement done by the proposed approach is found to be satisfactory and outperforms the same produced by other state-of-the-art algorithms through experiments on several noisy and poor contrast color images obtained from different standards databases.

Histogram equalization, which aims at information maximization, is widely used in different ways to perform contrast enhancement in images. In this paper, an automatic exact histogram specification technique is proposed, and used for global and local contrast enhancement of images. The desired histogram is obtained by first subjecting the image histogram to a modification process and then by maximizing a measure that represents increase in information and decrease in ambiguity. In comparison to a few existing methods, the effectiveness of the proposed automatic exact histogram specification technique in enhancing contrasts of grayscale images is demonstrated through visual assessment of results.

In this paper, we propose Multilayer network-based Visual Interpreter (MuVI), a framework for visual interpretability of Convolutional Neural Networks (CNNs) based on their mapping into multilayer networks. The peculiarity of MuVI is that it constructs a pixel-level heatmap of the salient parts of an image processed by a CNN, where the importance of each pixel depends on all layers of the CNN and not only on the final ones, as in the existing approaches in the literature. MuVI first maps the CNN into a multilayer network. It then uses this representation to identify the parts of the CNN that most influence the prediction results by extracting those paths within the multilayer network whose nodes correspond to the most active areas of the feature maps. The weight of the paths is given by the sum of the weights of the arcs corresponding to the activations across all feature maps of the CNN; this characteristic allows MuVI to consider all layers of the CNN, not just the last ones. Finally, MuVI constructs the visual interpretability heatmap by selecting the paths with the highest weights. The experimental tests performed show that MuVI is able to achieve very satisfactory results in terms of AUC insertion (0.25), AUC deletion (0.11), % Increase in Confidence (12.32), Average Drop % (51.22), Pointing Game Accuracy (0.28) and Computation time ( 26 . 226 s ). These results, taking all these measures together, are better than those obtained by the classical approaches already proposed in the literature, such as SmoothGrad, Grad-CAM, Grad-CAM++, and RISE. They are also comparable to state-of-the-art approaches in the literature, such as Score-CAM and HSIC. • A framework for visual interpretability of CNNs through a multilayer network. • Approaches to compute heatmaps from multilayer networks. • Visual interpretability for VGG16 and Imagenet. • Computation of heatmaps with pixel-level rather than area-level precision. • Competitive performance of the proposed framework compared to existing ones.

A small target detection algorithm in infrared image by combining multi-response fusion and local contrast enhancement

A scheme for enhancing local contrast of ultrasound images based on multiscale morphology is presented in this paper. The conventional theoretical concept of local contrast enhancement using mathematical morphology has been applied in the field of ultrasound image processing. The main idea is to preserve speckle region unchanged and enhance tissue boundaries. The criterion of speckle region is defined from a similarity value obtained from histogram matching between the histogram in the processing window and a reference one derived from a speckle area. The intensity values of the scale-specific features of tissue boundaries area in the image extracted using multiscale top hat transformation are modified for achieving local contrast enhancement. Locally enhanced features are combined to reconstruct the final enhanced image. The proposed approach has been applied on a set of ultrasound images for testing its response and the result has been compared with that of other standard methods for getting performance.

Software was developed in Python for the digital processing of object images before their detection using the You Only Look Once (YOLO) artificial neural network. Detection was carried out on object images, specifically vehicles and other road users. The preprocessing of images was performed through histogram equalization and local contrast enhancement. The original images were read from video cameras or graphic files. Image detection was performed using the medium-sized YOLO convolutional neural network model, YOLOv8m. As a result of detection, the studied objects, particularly cars, were highlighted with rectangular bounding boxes. To evaluate the accuracy of object detection, several metrics (parameters) were used: Precision, Recall, Intersection over Union (IoU), F1, and Average Precision (AP). The YOLO network was pre-trained on the Common Objects in Context dataset. The YOLO neural network returns the object recognition confidence, the coordinates of its center (x, y) on the image, and its width and height (w, h). The detected objects are marked with rectangular bounding boxes. The positioning of objects in the images was manually performed using the Roboflow tool, which allows for accurate spatial positioning of each object. The study of the impact of image preprocessing on the accuracy of object detection by the YOLO network was conducted using a typical traffic scene image. After histogram equalization, the histogram becomes more symmetrical and uniform, improving the visual quality of both dark and bright areas. This led to improved object detection accuracy across almost all metrics. The increase in detection accuracy by the IoU parameter is 0.169. The summary improvement in detection accuracy by the IoU, F1, AP parameters is 0.502. It was shown that after image preprocessing, detection accuracy increased, and all vehicles were correctly detected. The developed software can be used in various computer vision systems as well as in Internet of Things systems for remote monitoring and control.

AiTR is well developed field of R/D. Nonetheless. a relatively poor under-sampled infrared video may achieve a sharper imagery by smart pre-processing, similar to super-resolution attempts; the difference is in the details. We took a local adaptive contrast enhancement to exploit the pixel intensity correlation, such as smoothness, contrast, and continuity, among neighborhood pixels of variable region sizes, so-called adaptive local contrast enhancement. The final success or failure rate of AiTR will depend on the choice of cost function, such as LMS, etc. In that, we found that a sparse samples do not satisfy the usual underlying Gaussian assumption, of which the Maximum Likelihood, the Bayesian, the Fisher Rao criteria, etc. are usually depending on a priori assumption of dense sampling approaching the Gaussian statistics. Thus, in this paper, we have developed a sparse sampling classifier, called the min-Max classifier for Aided Target Recognition (AiTR), to minimize the intra-class dispersion and at the same to maximize the inter-class separation to select the optimum features vectors. As a standard test case, we choose Petland eigen-faces to benchmark our performance. We apply Szu's lossless divide and conquer theorem solving the NP Complete TSP solution to treat the multiple classes AiTR, in order to achieve min-Max classifier more efficiently than pair-wise SVM classifier.