Review Paper on Image Enhancement Using Various Methods

Image enhancement is crucial in medical imaging. Histogram equalization is an image enhancement technique employed to enhance image contrast, which has become a vital part of general and medical image processing. Although it is widely studied and applied, traditional histogram equalization achieves poor image enhancement results because it does not consider hue preservation. This study proposes a novel image enhancement method that incorporates hue preservation to address this problem. The results show that, compared with the equalized image of each RGB color channel using the traditional method, the proposed method yields superior results, with higher accuracy in terms of mean squared error and peak signal-to-noise ratio, when applied to retinal and prostate cancer images. This can effectively assist physicians in making the proper judgment.

Image enhancement is a fundamental step and plays an important role in image processing, pattern recognition, and computer vision. Image enhancement can sharpen the edges of objects in an image, making it easier to extract objects and attain more information from the enhanced image. In this paper, a few popular image enhancement methods based Fusion Method 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.

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.

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.

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

Image enhancement, aimed at improving the image contrast and information quality, is one of the most critical steps in image processing. Due to insufficient enhancement and the mean shift problem of conventional image enhancement techniques, new artificial intelligence-based image enhancement approaches have become an inevitable need in image processing. This paper employs the krill herd algorithm (KHA) and particle swarm optimization (PSO) to suggest a novel hybrid approach, called (PSOKHA) for image enhancement. The suggested PSOKHA method is used in search of optimum transfer function parameters to increase the quality of the images. For comparative evaluation, the performance of the PSOKHA is compared with six latest successful enhancement methods: PSO, KHA, screened Poisson equation (SPE), histogram equalization (HE), brightness preserving dynamic fuzzy HE (BPDFHE), and adaptive gamma correction weighted distribution (AGCWD). Experiments results in testing images include a medical image, a satellite image, and a handwritten image, demonstrate that the suggested strategy can produce better enhanced images in terms of several measurement criteria such as contrast, PSNR, entropy, and structure similarity index (SSIM).

In image processing applications, one of the main preprocessing phases is image enhancement that is used to produce high quality image or enhanced image than the original input image. These enhanced images can be used in many applications such as remote sensing applications, geo-satellite images, etc. The quality of an image is affected due to several conditions such as by poor illumination, atmospheric condition, wrong lens aperture setting of the camera, noise, etc [2]. So, such degraded/low exposure images are needed to be enhanced by increasing the brightness as well as its contrast and this can be possible by the method of image enhancement. In this research work different image enhancement techniques are discussed and reviewed with their results. The aim of this study is to determine the application of deep learning approaches that have been used for image enhancement. Deep learning is a machine learning approach which is currently revolutionizing a number of disciplines including image processing and computer vision. This paper will attempt to apply deep learning to image filtering, specifically low-light image enhancement. The review given in this paper is quite efficient for future researchers to overcome problems that helps in designing efficient algorithm which enhances quality of the image.

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.

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.

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.

As an attempt to introduce interactive, content‐dependent adaptive (ICDA) image processing, a simple but powerful active image sensing and two image enhancement methods are introduced via adaptive CNN‐UM sensor‐computers. Thus, the method ICDA can be used for adaptive control of image sensing and for subsequent on‐line or off‐line image enhancement as well. The algorithms use both intensity and contrast content. The image sensing technology can be realized with the current CNN‐UM chip. Our first image enhancement method is also executable on this chip, but it is more suitable for the adaptive cellular neural network universal machine (ACNN‐UM) architecture. Some results of simulator and chip experiments and an adaptive extended cell are presented. Our second, dynamical image enhancement method is planned to be executable on a multi‐layer, complex cell CNN architecture. In (Proceedings of the 6th IEEE International Workshop on Cellular Neural Networks and their Applications(CNNA‐2000) Catania, 2000; 213–217) 3‐layer architecture is described which is capable of realizing the main part of the second enhancement method.The main issues of our paper are as follows: the novel outlook of the ICDA framework, three new methods for two key application areas of CNN‐UM, the notion of ‘regional’ adaptive computing, the novelty of application of equilibrium‐computing in the third method. However, the key novelty of our work is not just a new method and a new realization: by combining sensing and computing, dynamically and pixelwise, a new quality becomes practical. Copyright © 2002 John Wiley & Sons, Ltd.

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.

Poor visibility not only affects the consumer photography but also computer vision application. Low-light image enhancement explains the image details or perception of information in images for human viewers and understanding of imagery. Using the pixel values existing in the digital image, the details of low-light images are boosted under low illumination. The primary purpose of enhancing low-vision image is to increase visibility of the image. Dealing with noise is a significant challenge while enhancing. Previous studies have offered a variety of approaches for enhancing low-vision images. Low contrast, low brightness, severe noise, dark colors are the issues with image enhancing in low-light situations. Monochromatic atmospheric scattering model is used to form proposed absorption light scattering model. The concealed details and hidden features were reproduced with sufficient and homogeneous illumination. This strategy will help to achieve high result in terms of structure, details and local feature similarity. Here, some mathematical formulas were used to produce brightest image, which is produced from monochromatic ASM and absorption light scattering model. When we compare absorption light scattering method with other techniques, it will produce better result. This strategy highlights major details of absorption light scattering model. This method will produce better result in terms of contrast, brightness, sharpness, etc.

A novel image enhancement method using fuzzy Sure entropy