Medical Image Enhancement Using Modified Color Histogram Equalization

Image enhancement methods were used to modify the images that were accepted as visual perception. Image enhancement methods mainly used for image contrast. Histogram compensation, used in the form of the cumulative distribution function (CDF), is one of the challenging factors in image processing. The goal of the enhancement is to improve the structural look of an image without impairing the input image. The enhancement techniques make it easier to identify key features by removing noise and other artifacts from an image. This article discusses studies to implement image enhancement in specific areas such as satellite imagery, infrared imagery, medical imagery and digital entertainment imagery, as well as information from the image enhancement method used. Keywords: Image enhancement, AHE, Histogram Equalization, CLAHE, spatial domain image, frequency domain method, etc. Cite this Article: Manish Sharma, Ashish Dubey, Prashant Badal. Review Paper on Image Enhancement using Various Methods. International Journal of Image Processing and Pattern Recognition. 2020; 6(1): 27–35p.

Medical image fusion based on enhanced three-layer image decomposition and Chameleon swarm algorithm

Image enhancement can be considered as one of the fundamental processes in image analysis. The goal of contrast enhancement is to improve the quality of an image to become more suitable for a particular application. Till today, numerous image enhancement methods have been proposed for various applications and efforts have been directed to further increase the quality of the enhancement results and minimize the computational complexity and memory usage. In this paper, an image enhancement methods based on Histogram Equalization (HE) was studied. This paper presents an exhaustive review of these studies and suggests a direction for future developments of image enhancement methods. Each method shows the owned advantages and drawbacks. In future, this work will give the direction to other researchers in order to propose new advanced enhancement techniques.

Acoustic images captured by side scan sonar are normally affected by speckle noise for which the enhancement is required in different domain. The underwater acoustic images obtained using sound as a source, basically contain seafloor, sediments, living and non-living resources. The Multiresolution based image enhancement techniques nowadays play a vital role in improving the quality of the low resolution image with repeated patterns. Image pyramid is the representation of an image at various scales. In this work, a three level Gaussian and Laplacian pyramids are constructed to represent the image in different resolution. The multiscale representation requires different filters at different scales. The contrast of each image in Gaussian and Laplacian pyramids are improved by applying both histogram equalization and unsharp masking method. The sharpened images are used to reconstruct the enhanced image. The performance measure, peak signal to noise ratio proves that the unsharp masking method applied to difference images of Laplacian pyramid outperforms the other image enhancement methods.

Digital image processing is versatile research in this era. Many researchers implement different types of organizations like image restoration, image enhancement, color image processing, image segmentation etc. Image enhancement technique is among the simplest and most appealing area of digital image processing. Enhancement techniques like brightness preservation, contrast enhancement highlight certain features means depend which part of the image want to be enhance some application some input image including noise, reduction or removal of noise is also form of image enhancement. Brightness preservation has enhanced visual quality of digital image so that the limitation contained in these images is used for various applications in a better way. A very popular technique for image enhancement is histogram equalization (HE) and curvelet transformation. HE technique is commonly employed for image enhancement because of its simplicity and comparatively better performance on almost all types of images. Another widely used technique is curvelet transformation. This technique is identified and separate bright regions of image but more error rate and low peak signal to noise ratio(PSNR), result of this technique is brightness preservation level is low and output image is gray. This paper design a hybrid model through discrete cosine transformation, discrete wavelet transformation and combine output of both techniques with image fusion. Proposed algorithm enhanced features and removal noise by decomposition of image using DWT and discrete cosine transformation, adaptive histogram equalization is very important part in this algorithm for smooth image. The tested results of different images are comparing with previous method, generating result with different parameters; less mean square error and high PSNR for improve the quality of an image. This paper presents a hybrid model used various parameter for enhance images like satellite images, medical images etc.

CCTV (Closed-circuit Television) is a surveillance camera that is installed on roads and in various rooms around the world with the aim of reducing crime and increasing public safety. Often, the image quality of CCTV has poor results, one of the causes is noise. This condition causes the information contained is sometimes not well conveyed. Image enhancement is a part of image processing which has the aim of improving image quality by manipulating image parameters. The application of this research is to sharpen the quality of screen capture on CCTV. The method applied is histogram equalization which can improve image quality. Histogram equalization method will be applied using Google Colab because its use has good portability.

This chapter focuses on the development of novel image enhancement and robust edge detection methods for practical medical image processing. It is known that the popular transformation-domain shrinkage approach for image enhancement applies a fixed mathematical basis to transform all images to be processed for noise or artifact reduction. However it is not adaptable to processed images, and then easily leads to blurring in the enhanced images. On the other hand, the techniques that are commonly used for edge detection are known as gradient and Laplacian operators (or mask), and smoothed gradient masks are typically used for edge detection in noisy images. However, these methods share a common major drawback wherein the associated masks are always fixed irrespective of the noise level in the images. In this study, we propose a novel learning-based method to adaptively deduce the transforming basis or masks from the processing data for medical image enhancement and robust edge detection. By using independent component analysis (ICA), the proposed learning-based method can extract suitable basis functions or masks for image transformation for processing data, which are adaptable to both the processed image and related noise in the image. The efficiency of the proposed learning-based method for medical image enhancement and edge detection is demonstrated experimentally using positron emission tomography (PET) and magnetic resonance imaging (MRI) medical images.

The purpose of this article is to describe effective methods in image enhancement and their application in processing gray scale as well as color images. Methods of image processing can be classified into two classes describing the image enhancement in the spatial domain (by gray‐scale transformations) and in the frequency domain. Examples of the first class are the well‐known methods of histogram equalization, single‐scale retinex, and multiscale retinex for grayscale and color images. The image enhancement in the frequency domain is mainly related to the application of discrete Fourier transform (DFT) with fast algorithms, although other transforms are also used. The methods of alpha‐rooting withDFTfor processing grayscale images and with quaternionDFTfor color images are described in this article, together with the method of retinex. The methods of image enhancement by the discrete signal‐induced heap transform are also presented. The quality of images is estimated by the known image enhancement measureEMEwith its generalizationEMECfor color images.

Automatic segmentation of the prostate of and the prostatic zones on MRI remains one of the most compelling research areas. While different image enhancement techniques are emerging as powerful tools for improving the performance of segmentation algorithms, their application still lacks consensus due to contrasting evidence regarding performance improvement and cross-model stability, further hampered by the inability to explain models’ predictions. Particularly, for prostate segmentation, the effectiveness of image enhancement on different Convolutional Neural Networks (CNN) remains largely unexplored. The present work introduces a novel image enhancement method, named RACLAHE, to enhance the performance of CNN models for segmenting the prostate’s gland and the prostatic zones. The improvement in performance and consistency across five CNN models (U-Net, U-Net++, U-Net3+, ResU-net and USE-NET) is compared against four popular image enhancement methods. Additionally, a methodology is proposed to explain, both quantitatively and qualitatively, the relation between saliency maps and ground truth probability maps. Overall, RACLAHE was the most consistent image enhancement algorithm in terms of performance improvement across CNN models with the mean increase in Dice Score ranging from 3 to 9% for the different prostatic regions, while achieving minimal inter-model variability. The integration of a feature driven methodology to explain the predictions after applying image enhancement methods, enables the development of a concrete, trustworthy automated pipeline for prostate segmentation on MR images.

Color image enhancement refers to improving the visual quality of color images by adjusting various attributes such as brightness, contrast, sharpness, and color balance. This enhancement aims to make the images more visually appealing, precise, and easier to interpret, often by accentuating essential features or details while minimizing noise or distortion. Techniques vary from simple adjustments of brightness and contrast to sophisticated algorithms. Effective image enhancement is crucial in numerous fields like medical imaging; enhancing the medical image is vital for accurate diagnosis and treatment planning, as well as remote sensing for improved satellite and aerial imagery. In security and surveillance, enhanced clarity in footage from surveillance cameras, especially in low-light scenarios, is crucial for identifying subjects and activities. However, image enhancement faces several challenges, like noise amplification and Over-enhancement, leading to unnatural-looking images with exaggerated or distorted features. In this paper, low-exposed or night color images are considered for enhancement, and this paper introduces exposure-based recursive histogram equalization techniques along with an Energy Curve instead of the conventional histogram; the energy curve is similar to a histogram based on spatial Contextual Information of an image. The proposed methods are Recursive Exposure-based Sub-image Histogram Equalization, Recursively Separated Exposure-based Sub-image Histogram Equalization, and Exposure-based Sub-image Histogram Equalization techniques considering spatial contextual information of images using an Energy Curve to improve results. These methods were tested on several publicly available datasetswith low-contrast color images and compared with HE, BBHE, DSHE, CLAHE, ESIHE, R-ESIHE, and RS-ESIHE. The proposed techniques are evaluated using parameterslikeAMBE, PSNR, MSE, Entropy, SSIM, and FSIM. The average PSNR values for eight images (taken from TeV DICeneter https://tev.fbk.eu/resources/imageenhancement) using the techniques mentioned above were 9.10817, 20.80568, 12.82645, 10.39347, 16.27458, 15.19979, and 14.59595, respectively. In contrast, the proposed ESIHE with Energy Curve achieved a PSNR of 22.31585, outperforming other methods across multiple metrics, particularly excelling in noise reduction (PSNR), error minimization (MSE: 193.44), structural similarity (SSIM: 0.733319), and feature retention (FSIM: 0.917678). The comparison demonstrates that enhancement methods can be significantly improved by considering spatial contextual information.

Compact Descriptor for Visual Search (CDVS) provides a standardized bitstream syntax for mobile image retrieval and matching applications. Although the standard CDVS algorithm has significant advances in the images with the good lighting condition, this approach often fails on dealing with the low-light images. A CDVS matching algorithm based on image enhancement is proposed. Firstly, a histogram equalization method is used for improving the quality of the pictures on low-light conditions to increase the matching number of the key points. Then, a same image enhancement method, which is mainly according to the homomorphic filtering, is adopt as the preprocessing for enhancing the contrast of the image with suppressing the low frequency signal and highlighting the high frequency signal to better extract the descriptors. Experimental results show that the proposed algorithm performs favorably against the standard CDVS algorithm on low-light images. Furthermore, the histogram equalization based CDVS matching algorithm has the best performance comparing with the homomorphic filtering method and the standard CDVS matching algorithm.

This paper presents a new image enhancement method using histogram equalization called Bi-Histogram Equalization with Non-parametric Modified Technology (BHENMT). Our proposed method consists of three steps: (i) The input original histogram is divided into two parts using the Otsu method. (ii) Then the histogram modification technique is used to control over enhancement and maximize entropy. (iii) Two sub images are enhanced by the tradtional histogram equalization method using the corresponding modified histogram respectively and finally are merged into one output enhanced image. The experimental results show that BHENMT is better than other contrast enhancement methods according to subjective evaluation and various image objective evaluation measures, i.e. Entropy, AMBE and PSNR.

A rigid body three-dimensional/two-dimensional (3D/2D) registration method has been implemented using mutual information, gradient ascent, and 3D texturemap-based digitally reconstructed radiographs. Nine combinations of commonly used x-ray and computed tomography (CT) image enhancement methods, including window leveling, histogram equalization, and adaptive histogram equalization, were examined to assess their effects on accuracy and robustness of the registration method. From a set of experiments using an anthropomorphic chest phantom, we were able to draw several conclusions. First, the CT and x-ray preprocessing combination with the widest attraction range was the one that linearly stretched the histograms onto the entire display range on both CT and x-ray images. The average attraction ranges of this combination were 71.3 mm and 61.3 deg in the translation and rotation dimensions, respectively, and the average errors were 0.12 deg and 0.47 mm. Second, the combination of the CT image with tissue and bone information and the x-ray images with adaptive histogram equalization also showed subvoxel accuracy, especially the best in the translation dimensions. However, its attraction ranges were the smallest among the examined combinations (on average 36 mm and 19 deg). Last the bone-only information on the CT image did not show convergency property to the correct registration.

Histogram equalization is an important image enhancement technique widely used in image processing. It is good method of image enhancement indeed; but it doesn't consider about human visual response to light. We know that human visual response to light is non-linear, which is somewhat likes natural logarithmic function. So the gray levels of histogram equalized, image is not really equally distributed for human eyes. In fact, the ordinary histogram equalization is not visually equalized. According to the human visual model, this article puts forward a new algorithm or image histogram modification, with which modified image has visually equal gray level distribution. This algorithm is conducted while assuming exiting a visual image whose histogram can be counted and is equal distributed. Experiment proves that this algorithm is better than ordinary image histogram equalization technique in image enhancement. It fits human visual response well. It can be widely used in digital image processing.

An magnified image enhancement algorithm is presented in this study. Image interpolation is an important image magnification tool, but the magnified image has not been changed by the traditional image interpolation methods even if the magnified image is not satisfaction. Basically, the interpolating surface is determined uniquely for the given interpolating data. Namely the interpolating surface (magnified image) is fixed when the interpolating data (gray value of original image) is given. If the magnified image needs to be enhanced, another enhancement method must be chosen after image magnifying. The image magnification and enhancement are separated. The image natural attribute will be effected. To overcome the disadvantages of the traditional methods, a new method which combined the image magnification and enhancement is proposed. A bivariate rational interpolation with parameters is used in the algorithm. The value of the interpolating function at any point in the interpolating region can be modified under the condition that the interpolating data are not changed by selecting the suitable parameters. Using the surface control, the enlarged image enhancement is implemented. The experiment shows that the algorithm is efficient.