Intelligent Image Enhancement System based on Similarity Pixels

The remote sensing images acquired from the satellites are low contrast images. The availability of low contrast images and failure of the traditional methods such as Histogram Equalization and Gamma correction in preserving the brightness levels in the image are the main issues in satellite image processing. This paper proposes an optimized contrast stretching using non-linear transformation for image enhancement. The non-linear transformation is influenced by the appropriate choice of control parameters for the sample images since manual tuning for individual images is tedious. A Bat algorithm based tuning is employed for the automated selection of control parameters in the transformation. The performance of the optimization algorithm is compared against other metaheuristic algorithms such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). It is noted that the bat algorithm based contrast enhancement outperforms the other optimization techniques in terms of metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy and CPU time (Central Processing Unit).

Intracerebral hemorrhage (ICH) and cerebral amyloid angiopathy (CAA) are two common causes of hemorrhagic stroke. Distinguishing CAA from ICH is challenging due to similarities in clinical presentation and imaging. Magnetic Resonance Imaging (MRI) is one imaging modality that can be used in the early detection of ICH. The objective of this paper is to identify the best enhancement techniques in MR images for ICH classification. 5 samples of MR images with a final diagnosis of primary ICH (pICH) were selected from UKM Medical Centre (UKMMC). The MR images which were in Digital Imaging and Communications in Medicine (DICOM) format were later processed using the Matrix Laboratory (MATLAB) software. In this study 5 MR images from 5 different patients were processed using 4 image enhancement techniques; power law transformation histogram equalization image sharpening and median filter. All the 5 processed images were compared to obtain the best enhanced images based on their Absolute Mean Brightness Error (AMBE) and entropy values. Median filter is the best image enhancement technique with average values of AMBE and entropy of 0.0885 and 5.1472.

Conventional contrast enhancement techniques often fail to produce satisfactory results for low-contrast images, and cannot be automatically applied to different images because their processing parameters must be specified manually to produce a satisfactory result for a given image. This work presents a colour-preserving contrast enhancement (CPCE) algorithm for images. Modification to images was performed in the HSV colour-space. The Hue component is preserved (unchanged), luminance modified using Contrast Limited Adaptive Histogram Equalization (CLAHE), while Saturation components were up-scaled using a derived mapping function on the approximate components of its discrete wavelet transform. Implementation was done in MATLAB and compared with CLAHE and Histogram Equalization (HE) algorithms in the RGB colour space. Subjective (visual quality inspection) and objective parameters (Peak-signal-to-noise ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Mean squared error (MSE)) were used for performance evaluation. The method produced images with the lowest MSE, AMBE, and highest PSNR when tested, yet preserved the visual quality of the image.

Contrast Enhancement is a technique which comes into the part of Image Enhancement. Contrast Enhancement is used to enhance the visual quality of any captured or other image. Contrast Enhancement can be performed with the help of Histogram equalization (HE). In this technique, the image is collected in the gray scale allocation. The image is then partitioning and applying adaptive Histogram equalization (AHE). Fuzzy logic provides a set of logics which enhance the contrast and visibility of any image. In this technique, the visual quality and the contrast of image will change and then compare these results with previous techniques. The performance of several established image enhancement techniques is presented in terms of different parameters like Absolute mean brightness error (AMBE), Peak signal to noise ratio (PSNR), contrast and Visual quality.

Mammograms are generally contaminated by noise which assures the need for image enhancement to aid interpretation. The enhancement of mammograms is a very important problem for easy extraction of suspicious regions known as regions of interest (ROIs). This paper introduces comparison of various hybrid enhancement algorithms based on mathematical morphology, contrast stretching, wavelet transform, anisotropic diffusion filter and contrast limited adaptive histogram equalization (CLAHE). The performances of algorithms have been compared by using three global image enhancement evaluation measures; Enhancement Measure (EME), Absolute Mean Brightness Error (AMBE) and Peak Signal-to-Noise Ratio (PSNR). For this study, we have used MIAS database. Experimental results show that the combination of mathematical morphology, anisotropic diffusion filter and CLAHE methods, yields significantly superior image quality and provides more visibility for the suspicious regions.

Contrast enhancement and brightness preservation of the image are two important issues of image enhancement in research field now-a-days. The objective is to enhance the image uniformly over different parts of the image. General Histogram Equalization doesn’t control degree of enhancement of the image. To overcome this drawback, another variant of Histogram Equalization method namely Adaptive Bi-histogram Equalization using Threshold (ABHET) is being proposed. The proposed method undergoes three steps, such as: Histogram segmentation using threshold, Clipping of histogram using mean value of occupied intensity and histogram equalization of each sub-images. Finally all the sub-images are combined into one complete image. Simulation results show that ABHET outperforms other existing HE-based methods and different image quality measures such as: Peak signal to noise ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Structural Similarity Index (SSIM) are being used to test the robustness of the proposed method in terms of enhancement of contrast and preservation of brightness.

This paper presents a comparative analysis of fundus image enhancement techniques to detect diabetic retinopathy (DR). Medical images frequently suffers from non-uniform illumination, poor contrast and noise, thus these images have to go through pre-processing stage. For image enhancement many techniques are proposed based on spatial domain (histogram). However, these methods usually decline to produce suitable consequences for non-uniform illumination and wide-ranging of low-contract. The comparative analysis and performance evaluation of various enhancement techniques will help in choosing most suitable technique which may significantly improve the detection of diabetic retinopathy. In this paper histogram equalization (HE), adaptive histogram equalization (ADHE), contrast limited adaptive histogram equalization (CLAHE) and exposure based sub-image histogram equalization (ESIHE) techniques are compared for pre-processing of fundus image for detecting DR. For fair analysis of these techniques, histogram, SNR, entropy, absolute mean brightness error and peak signal-to-noise ratio (PSNR) of fundus images are analyzed by using MATLAB.

A new approach based on masking technique for contrast enhancement of medical image is presented due to the low contrast characteristics. In medical image, preserving mean brightness, average information and noise factor reduction are essential to make input image more appealing visually. The proposed method incorporates spatial and frequency domain techniques to enhance the contrast of the medical image. The mask is formulated effectively between reconstructed approximation coefficients and inverse singular value decomposition (ISVD) to obtain contrast residual. Discrete wavelet transformation (DWT) and singular value decomposition (SVD) have used to decompose the input image. At last maximum contrast enhancement achieved by adding mask with the image obtained through intensity exposure histogram equalization (IEHE). The proposed approach is tested for medical images by comparing its peak signal to noise Ratio (PSNR), absolute mean brightness error (AMBE) and entropy with some existing methods and also measured in terms of visual quality.

This paper proposes the development and evaluation of an auto-tuning stochastic resonance (SR) for image enhancement on images under various illumination conditions. Perceptual Quality Metric (PQM) is used for evaluating and quantifying the image quality. The current process is developed being based on our previous works related to the image enhancement by using the manual tuning stochastic resonance. The process was performed by adding the random noise and threshold in an image. The process works properly in the dark and very low contrast images as well as bright images. This image enhancement system works on dark and bright images as well. The system was tested with the face detection algorithm on the dark and illumination variant images. In this paper, we present the idea of auto-tuning of the SR iteration with random noise and threshold value 0 by using the process related to the histogram calculation, mean and median. In this paper, we performed various experiments on object and human detection as well under different conditions and confirmed the effectiveness of our auto-tuning SR based image enhancement algorithm. Finally, we conducted experiments using Perceptual Quality Metric which is an image quality metric to apply the proposed algorithm in various fields.

Image enhancement is important in different application areas such as medical imaging, computer graphics, and military applications. In this paper, we introduce a dataset with enhanced images. The images have been enhanced by five end users, and these have been evaluated by observers in an online image quality experiment. The enhancement steps by the end users and subjective results are analysed in detail. Furthermore, 38 image quality metrics have been evaluated on the introduced dataset to reveal their suitability to measure image enhancement. The results show that the image quality metrics have low to average performance on the new dataset.

Image enhancement/contrast adjustment plays an important role in almost every image processing system. The main aim of the contrast adjustment is to enhance image quality by maximizing the information content in the image. Many researchers implemented heuristic approaches for image enhancement using maximization of objective function based on image quality metrics. And such objective function may fail to yield proper enhancement for a given image (either under enhanced or over enhanced). Because the parameters of the image transformation function are selected based on overall value of the objective function but not individual objectives in the objective function. That means the parameter values so chosen may not satisfy all the objectives of the objective function, and this may lead to improper enhancement of the image. In this context, this paper presents a Pareto optimal approach-based gray level contrast adjustment using Improved Differential Search Algorithm (IDSA) named Multi-Objective Improved Differential Search Algorithm (MOIDSA). Image enhancement is treated as a three-dimensional optimization problem, and MOIDSA is used to solve it. The input image quality is enhanced by simultaneous objectives which is a blend of image performance measures and quality metric as a three-dimensional optimization approach will yield best-compromised solution by satisfying all the individual objectives. This algorithm is tested on some standard test gray level images, and results compared with well existing algorithms have proven its superiority.

Glaucoma is the second largest cause of permanent blindness all over the world. Glaucoma is marked by change in shape and size of optic disc (OD) and optic cup (OC). Contrast enhancement is necessary to improve the visuality of OD and OC in images for early detection, screening and timely treatment planning of glaucoma. Morphological filters are adapted for the contrast enhancement of the retinal fundus images. Combination of morphological filters such as top-hat transform and bottom-hat transform are considered for enhancement of retinal fundus images. The quality of the improvement of the image can be determined by the use of measure of enhancement (EME). The resultant enhanced images have been obtained with the increased value of EME indicating that enhancement has been done.

The contrast of an image is a feature which determines how image looks better visually. In this paper, we are analysing the capability of activation functions for contrast enhancement. Activation functions are classically used in neural network. In this paper, Activation function creates a mask which is operated on the image on pixel by pixel basis. On the basis of activation function the pixel value of image is changed which improves the contrast of image. We have used various activation functions such as sigmoid function, bipolar sigmoid function, RAMP function, hyperbolic tangent function. Contrast enhancement using these activation functions has been successfully applied on several dark and bright images. For performance assessment we have used Peak Signal to Noise Ratio (PSNR), absolute mean brightness error (AMBE), and Structure Similarity Index (SSIM). From experimental result, it is observed that RAMP function and hyperbolic tangent function have better image enhancement capability.

Real time video image enhancement approach using particle swarm optimisation technique with adaptive cumulative distribution function based histogram equalization

Histogram Equalization (HE) is a simple, effective and widely used contrast enhancement technique as it can automatically define the intensity transformation function based on statistical characteristics of the image, but it tends to change the mean brightness of the image to the middle level of the gray level range. HE also produces saturation effects by extremely pushing the intensities towards the right or the left side of the histogram. To surmount these drawbacks, Clipping or Plateau Histogram Equalization techniques for brightness preserving and contrast enhancement have been proposed, but, these are not suitable for automatic systems because of manual selection of threshold level. Self-Adaptive Plateau Histogram Equalization (SAPHE) selects the threshold level automatically, but the process is relatively complicated and sometimes fails in execution. To overcome these drawbacks, a Modified Self - Adaptive Plateau Histogram Equalization with Mean threshold (Modified SAPHE-M) is proposed in this paper and compared the experimental results with Histogram Equalization (HE), Self-Adaptive Plateau Histogram Equalization (SAPHE) and Modified Self-Adaptive Plateau Histogram Equalization (Modified SAPHE) by using image quality measures such as Absolute Mean Brightness Error (AMBE) and Peak-Signal to Noise Ratio (PSNR).