Image Enhancement using Guided Filter for under Exposed Images

Image enhancement becomes an important step to improve the quality of image and change in the appearance of the image in such a way that either a human or a machine can fetch certain information from the image after a change. Due to low contrast images it becomes very difficult to get any information out of it. In today’s digital world of imaging image enhancement is a very useful in various applications ranging from electronics printing to recognition. For highly underexposed region, intensity bin are present in darken region that’s by such images lacks in saturation and suffers from low intensity. Power law transformation provides solution to this problem. It enhances the brightness so as image at least becomes visible. To modify the intensity level histogram equalization can be used. In this we can apply cumulative density function and probabilistic density function so as to divide the image into sub images. In proposed approach to provide betterment in results guided filter has been applied to images after equalization so that we can get better Entropy rate and Coefficient of correlation can be improved with previously available techniques. The guided filter is derived from local linear model. The guided filter computes the filtering output by considering the content of guidance image, which can be the image itself or other targeted image

Enhancement of Medical Images using Histogram Based Hybrid Technique

Digital Image Processing is very important area of research. A number of techniques are available for image enhancement of gray scale images as well as color images. They work very efficiently for enhancement of the gray scale as well as color images. Important techniques namely Histogram Equalization, BBHE, RSWHE, RSWHE (recursion=2, gamma=No), AGCWD (Recursion=0, gamma=0) have been used quite frequently for image enhancement. But there are some shortcomings of the present techniques. The major shortcoming is that while enhancement, the brightness of the image deteriorates quite a lot. So there was need for some technique for image enhancement so that while enhancement was done, the brightness of the images does not go down. To remove this shortcoming, a new hybrid technique namely RESWHE+AGCWD (recursion=2, gamma=0 or 1) was proposed. The results of the proposed technique were compared with the existing techniques. In the present methodology, the brightness did not decrease during image enhancement. So the results and the technique was validated and accepted. The parameters via PSNR, MSE, AMBE etc. are taken for performance evaluation and validation of the proposed technique against the existing techniques which results in better outperform

Multipurpose contrast enhancement on epiphyseal plates and ossification centers for bone age assessment

BACKGROUND: The high variations of background luminance, low contrast and excessively enhanced contrast of hand bone radiograph often impede the bone age assessment rating system in evaluating the degree of epiphyseal plates and ossification centers development. The Global Histogram equalization (GHE) has been the most frequently adopted image contrast enhancement technique but the performance is not satisfying. A brightness and detail preserving histogram equalization method with good contrast enhancement effect has been a goal of much recent research in histogram equalization. Nevertheless, producing a well-balanced histogram equalized radiograph in terms of its brightness preservation, detail preservation and contrast enhancement is deemed to be a daunting task. METHOD: In this paper, we propose a novel framework of histogram equalization with the aim of taking several desirable properties into account, namely the Multipurpose Beta Optimized Bi-Histogram Equalization (MBOBHE). This method performs the histogram optimization separately in both sub-histograms after the segmentation of histogram using an optimized separating point determined based on the regularization function constituted by three components. The result is then assessed by the qualitative and quantitative analysis to evaluate the essential aspects of histogram equalized image using a total of 160 hand radiographs that are implemented in testing and analyses which are acquired from hand bone online database. RESULT: From the qualitative analysis, we found that basic bi-histogram equalizations are not capable of displaying the small features in image due to incorrect selection of separating point by focusing on only certain metric without considering the contrast enhancement and detail preservation. From the quantitative analysis, we found that MBOBHE correlates well with human visual perception, and this improvement shortens the evaluation time taken by inspector in assessing the bone age. CONCLUSIONS: The proposed MBOBHE outperforms other existing methods regarding comprehensive performance of histogram equalization. All the features which are pertinent to bone age assessment are more protruding relative to other methods; this has shorten the required evaluation time in manual bone age assessment using TW method. While the accuracy remains unaffected or slightly better than using unprocessed original image. The holistic properties in terms of brightness preservation, detail preservation and contrast enhancement are simultaneous taken into consideration and thus the visual effect is contributive to manual inspection

A Convex Model for Edge-Histogram Specification with Applications to Edge-preserving Smoothing

The goal of edge-histogram specification is to find an image whose edge image has a histogram that matches a given edge-histogram as much as possible. Mignotte has proposed a non-convex model for the problem [M. Mignotte. An energy-based model for the image edge-histogram specification problem. IEEE Transactions on Image Processing, 21(1):379--386, 2012]. In his work, edge magnitudes of an input image are first modified by histogram specification to match the given edge-histogram. Then, a non-convex model is minimized to find an output image whose edge-histogram matches the modified edge-histogram. The non-convexity of the model hinders the computations and the inclusion of useful constraints such as the dynamic range constraint. In this paper, instead of considering edge magnitudes, we directly consider the image gradients and propose a convex model based on them. Furthermore, we include additional constraints in our model based on different applications. The convexity of our model allows us to compute the output image efficiently using either Alternating Direction Method of Multipliers or Fast Iterative Shrinkage-Thresholding Algorithm. We consider several applications in edge-preserving smoothing including image abstraction, edge extraction, details exaggeration, and documents scan-through removal. Numerical results are given to illustrate that our method successfully produces decent results efficiently

Contrast Enhancement and Brightness Preservation of Radiography Images using Gamma Correction

In this Research paper, the purpose of Image enhancement is to process an image so that result is more suitable than original image for particular application. Digital image enhancement techniques provide a multitude of choices for improving the visual quality of images. Appropriate variety of such techniques is greatly influenced by the imaging modality, undertaking at hand and viewing situation. This paper will provide an overview of underlying concepts, along with algorithms normally used for image enhancement. An image can have low contrast or undesirable quality due to a number of reasons like reduced quality of imaging device, unfavorable external conditions at the time of image preprocessing and many more. Image enhancement is used to improve the usual effects and clarity of image or to make the original image more favorable for computer to process. In proposed method, full image has been divided into two parts in low contrast and high contrast on the basis of their threshold value. We have applied large gamma values only on low contrast image using gamma correction which will be more informative. With the help of merging we get the resultant radiography image. The results will be compared on the basis of histograms, mean, standard deviation, variance and average gradient values and compared with existed gamma correction techniques using matlab

Image Enhancement Based on Histogram Equalization with Linear Perception Neural Network Method

Image enhancement poses a formidable challenge in low-level image processing. While various strategies, such as histogram equalisation, multipoint histogram equalisations, and picture element-dependent contrast preservation, have been employed, the efficacy of these approaches has not consistently met expectations. In response, this paper proposes a novel image enhancement method based on a linear perception neural network, demonstrating superior results in contrast improvement with brightness preservation. The proposed method leverages the interdependence of image components through a linear perceptron network, incorporating curvelet transform for image transformation into a multi-resolution mode. This transformative approach identifies component differences in picture elements, establishing a dependency characteristic matrix as a weight vector for the perceptron network. The perceptron network dynamically adjusts the weights of input image values, enhancing contrast while preserving brightness. Extensive testing of the image interdependence linear perception neural network method for contrast improvement has been conducted on multiple images. To quantify brightness preservation, comparative analysis with existing image enhancement strategies, such as histogram equalisation, was performed using Absolute Mean Brightness Error (AMBE) metrics. A smaller AMBE value indicates better preservation, while the Peak signal-to-noise ratio (PSNR) was employed to measure contrast improvement, with higher PSNR values indicating superior results. The proposed method (LPNNM) was rigorously evaluated against the conventional histogram equalisation (HE) technique for image enhancement. The results demonstrated that the LPNNM method outperforms HE in terms of both brightness preservation (as indicated by AMBE) and contrast improvement (as indicated by PSNR). This research contributes a robust and effective solution to the challenge of image enhancement, offering a more advanced alternative to existing methodologies