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

Feature Extraction using Normalized Difference Vegetation Index (NDVI): A Case Study of Jabalpur City

AbstractThis paper presents an improved method for the analysis of satellite image based on Normalized Difference Vegetation Index (NDVI). The method employs the multi-spectral remote sensing data technique to find spectral signature of different objects such as vegetation index, land cover classification, concrete structure, road structure, urban areas, rocky areas and remaining areas presented in the image. For land cover classification, some band combinations of the remote sensed data are exploited and the spatial distribution such as road, urban area, agriculture land and water resources are easily interpreteted by computing their normalized difference vegetation index. Different values of threshold of NDVI are used for generating the false colour composite of the classified objects. The simulation results show that the NDVI is highly useful in detecting the surface features of the visible area which are extremely beneficial for municipal planning and management. The vegetation analysis can be used for the situation of unfortunate natural disasters to provide humanitarian aid, damage assessment and furthermore to device new protection strategies

Weighted contrast enhancement based enhancement for remote sensing images

This paper discuss a novel approach based on dominant brightness level analysis and adaptive intensity transformation to enhance the contrast for remote sensing images. In this approach  we first perform discrete wavelet (DWT) on the input images and then decompose the bLL sub band into low-, middle-, and high-intensity layers using the log-average luminance. After estimating the intensity transformation, the resulting enhanced image is obtained by using the inverse DWT. The proposed algorithm overcomes this problem using the adaptive intensity transfer function. The experimental results show that the proposed algorithm enhances the overall contrast and visibility of local details better than existing techniques

A Novel Technique for Fundus Image Contrast Enhancement

ABSTRACT Digital fundus Image analysis plays a vital role in computer aided diagnosis of several disorders. Image acquired with fundus camera often have low grey level contrast and dynamic range .We present a new method for fundus image contrast enhancement using Discrete Wavelet Transform (DWT) and Singular Value Decomposition(SVD).The performance of this technique is better than conventional and state of the art-techniques. With the proposed method the given Fundus Image is decomposed into four frequency sub band images and Singular Value Decomposition applied on Low-Low subband Image, which determines the intensity information. Finally Image reconstructed using modified Low-Low subband coefficients and three high frequency sub band coefficients. The qualitative and quantitative performance of proposed technique i

APPLICATION OF REMOTE SENSING IN MONITORING FOREST COVER CHANGE AND CARBON DIOXIDE LEVELS AT KISATCHIE NATIONAL FOREST OF LOUISIANA

It is estimated that the globe’s forest has shrunk by 3% since 1990, an area equivalence to the geographical boundaries of South Africa. The Kisatchie National Forest of Louisiana replicates plentiful climatic, physiographic and edaphic differences in the country and this forest faces a serious problem of degradation and disturbance of different nature. Remote sensing from satellites offers the best way to observe these changes over time. This study will employ Landsat-8 satellite imagery to analyze forest cover change in Kisatchie National Forest from 2010 to 2020. The objectives of the study are to (i) identify the trend, nature, and the magnitude of forest cover change, (ii) prepare image maps delineating forest cover change for the duration of the study (iii) establish the trend of CO2 levels within Kisatchie environs. Results showed a gain of forest cover within the Kisatchie National Forest which correlated to the rate of CO2 sequestration by sinks. NDVI of 2010 was 0.65 compared to 0.86 for 2020 indicating a gain of 32% of forest cover since 2010. This showed how effective Protected areas are in conserving forest cover and restricting land uses that may disturb forest structure

Gaussian mixture model-based contrast enhancement

In this study, a method for enhancing low-contrast images is proposed. This method, called Gaussian mixture model-based contrast enhancement (GMMCE), brings into play the Gaussian mixture modelling of histograms to model the content of the images. On the basis of the fact that each homogeneous area in natural images has a Gaussian-shaped histogram, it decomposes the narrow histogram of low-contrast images into a set of scaled and shifted Gaussians. The individual histograms are then stretched by increasing their variance parameters, and are diffused on the entire histogram by scattering their mean parameters, to build a broad version of the histogram. The number of Gaussians as well as their parameters are optimised to set up a Gaussian mixture modelling with lowest approximation error and highest similarity to the original histogram. Compared with the existing histogram-based methods, the experimental results show that the quality of GMMCE enhanced pictures are mostly consistent and outperform other benchmark methods. Additionally, the computational complexity analysis shows that GMMCE is a low-complexity method