Colour image enhancement based on wavelet approximation coefficient histogram

In color image enhancement, gamut problem is one of the fundamental issues for practical image processing tasks. However, recent color image enhancement methods without gamut problem fail to preserve or enhance color saturation, which is an essential property for visual perception. In this paper, we propose a color image enhancement method, in which we introduce a new histogram named aggregated histogram (AgH), which is computed with R, G and B components of all pixels in a color image, and then equalize AgH to obtain an enhanced color image in a similar manner to conventional grayscale histogram equalization (GHE). As a result, the proposed method is free from gamut problem as well as GHE. Moreover, we extend the proposed AgH equalization (AgHE) method to adaptive AgHE (AAgHE), which equalizes a local histogram at each pixel of a color image. Experimental results show that the proposed methods produce better results than existing color image enhancement methods. Consequently, the proposed methods can be a good alternative to conventional color image enhancement methods without gamut problem.

Histogram equalization (HE) is one of the simplest and most effective techniques for enhancing gray-level images. For color images, HE becomes a more difficult task, due to the vectorial nature of the data. We propose a new method for color image enhancement that uses two hierarchical levels of HE: global and local. In order to preserve the hue, equalization is only applied to intensities. For each pixel (called the ‘‘seed’’ when being processed) a variable-sized, variable-shaped neighborhood is determined to contain pixels that are ‘‘similar’’ to the seed. Then, the histogram of the region is stretched to a range that is computed with respect to the statistical parameters of the region (mean and variance) and to the global HE function (of intensities), and only the seed pixel is given a new intensity value. We applied the proposed color HE method to various images and observed the results to be subjectively ‘‘pleasant to the human eye,’’ with emphasized details, preserved colors, and with the histogram of intensities close to the ideal uniform one. The results compared favorably with those of three other methods (histogram explosion, histogram decimation, and three-dimensional histogram equalization) in terms of subjective visual quality.

A novel method for color image enhancement is proposed as an extension of the scalar-diffusion-shock-filter coupling model, where noisy and blurred images are denoised and sharpened. The proposed model is based on using the single vectors of the gradient magnitude and the second derivatives as a manner to relate different color components of the image. This model can be viewed as a generalization of the Bettahar-Stambouli filter to multivalued images. The proposed algorithm is more efficient than the mentioned filter and some previous works at color images denoising and deblurring without creating false colors.

Natural color image enhancement and evaluation algorithm based on human visual system

An Image Resolution Enhancement Technique based on Interpolation of the high frequency sub-band of colour images obtained by Discrete Wavelet Transform and the input colour image is proposed in this paper. Interpolation determines the intermediate values on the basis of observed values. One of the commonly used interpolation technique is Bicubic Interpolation. The edges are enhanced by introducing an intermediate stage by using Stationary Wavelet Transform. It is designed to overcome the lack of Translation-Invariance of Discrete Wavelet Transform. This is widely used in Signal Denoising and Pattern Recognition. Discrete Wavelet Transform is applied in order to decompose an input colour image into different sub-bands. Then the high frequency sub-bands as well as the input colour image are interpolated separately. The interpolated high frequency sub-bands and the Stationary Wavelet Transform high frequency sub-bands have the same size which means they can be added with each other. The new corrected high frequency sub-bands can be interpolated further for higher enlargement. Then all these sub-bands are combined with interpolated input image for new high resolution image by using Inverse Discrete Wavelet Transform. This has been done by MATLAB. The Peak Signal-Noise Ratio was obtained upto 5dB greater than the conventional and state-of-art image resolution enhancement techniques.

Automatic color equalization (ACE) algorithm is an effective method for color image enhancement, but its computational complexity is extremely high. In this paper, we first modify the ACE algorithm to reduce the computational complexity and realization cost while maintaining good visual quality. Subsequently, an efficient VLSI architecture for the hardware-friendly ACE algorithm is proposed to meet the requirement of real-time image enhancement. FPGA (Field Programmable Gate Arrays) implementation result shows that the proposed architecture can operate at 120MHz and achieve a throughput of 60 frame/s for 256×256 resolution images using about 1.15k and 1.78k of FPGA's logic (LUT) and register resources, respectively. Compared with the existing design, the proposed architecture can achieve higher performance with fewer hardware resources and comparable visual quality.

We present a new image watermarking technique for color images. Tradeoff between imperceptibility, robustness, and security is achieved by this technique. This technique is based on discrete wavelet transform (DWT) and singular valued decomposition (SVD). Advantage of both DWT and SVD has been utilized to achieve more robustness. Differential evolution (DE) is used for optimization of scaling factors. DE is incorporated to find optimal scaling factors to scale down the watermark and increase the visual quality and robustness of watermarked image. Color cover image is split into three color channels using RGB color space. To improve the security, watermark is scrambled before embedding using random Bit-Plane and XOR operation. Experimental results show that the watermarked image is imperceptible and resistant to various geometric attacks and image processing.

For an effective communication system whether indoor or outdoor, the most important concern is minimum noise. In this paper, an efficient noise reduction technique is presented using various wavelet transform techniques for indoor optical wireless communication system (IOWC). In IOWC system, Fluorescent Light Interference (FLI) is main source of noise. Here, in this paper three methods are used to reduce the effect of noise from a digital signal. These are Discrete Wavelet Transform (DWT), Stationary Wavelet transform (SWT) and Discrete Wavelet transform-Stationary Wavelet Transform (DWT-SWT). Through sub band coding in DWT the signal is decomposed into lower sub bands of high and low frequency respectively of unequal size; while in SWT the decomposed signal have sub bands of equal size. In DWT-SWT the high frequency components of both DWT and SWT are added. Using Pulse Position Modulation, the comparison between these three techniques is described here to enhance the overall performance of the IOWC system.

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.

In order to enhance the resolution of the image and improve the visual effect of the image, a method of image quality enhancement based on bilinear interpolation and wavelet transformation is proposed. The algorithm first enhances the image by histogram equalization and bilinear interpolation to preserve better detail quality, and then perform wavelet transform on the histogram equalized image and the original imageeach of the input images are devided into 4 subbands, estimates the singular value matrix of the low frequency subband image, and then enhances the image through Wavelet reconstruction, Finally, perform histogram matching on the result image and bilinear interpolation image to obtain an enhanced image. The experimental results show that the performance of the proposed method is better than that of the traditional method.

To acquire the fruit region of green apple image in close color more completely, this work designed a method of region extraction before fruit region combination. Firstly, channel images in every color space were analyzed to determine appropriate color channel images: images of G and b color channel in RGB and Lab color spaces, respectively. Then, these images were segmented by K-means clustering to obtain segmented images of G and b color channel. Post-processing was conducted to different segmentation results. For example, open operation and micro-region elimination were conducted to segmented image of G color channel. The same processing was conducted to b color channel image after combining with above processed G color channel image and filling holes. Therefore, the target fruit region can be acquired completely. The validity of this method was verified by experiment.

In this paper, we present a new feature extraction method for Electroencephalogram (EEG) signals classification, based on GARCH modeling of wavelet coefficients. First, the EEG signals are decomposed into the frequency sub-bands using discrete wavelet transform (DWT). GARCH model can capture the important statistical properties of wavelet coefficients. We demonstrate that Autoregressive Conditional Heteroscedastcity (GARCH) effect exists in wavelet coefficients of EEG signals. Then GARCH variance series are calculated from each sub-band of wavelet coefficients. After that, a set of statistical features are extracted from variance series to represent of variance series. We use linear discriminate analysis (LDA) for feature selection. Resultant features are classified by multilayer perceptron (MLP) with three discrete outputs: healthy volunteers, epilepsy patients during seizure-free interval and epilepsy patients during seizure. Experimental results show that by using GARCH model on the wavelet coefficients, correct classification rate (CCR) is improved.

Digital image-processing technique is used in image super-resolution for a variety of applications. In this paper, we propose hybrid domain feature-based image super-resolution using a fusion of Average Pixel Values Technique (APVT) and Discrete Wavelet Transform (DWT). The low-resolution (LR) images are considered and converted into high-resolution (HR) images using a novel technique of APTV by inserting an average of rows between rows and average of columns between columns to get HR images. The DWT is applied on HR images to obtain four bands. The HR images are downsampled, and to enhance image quality, histogram equalization (HE) is utilized. The LL band and HE matrix are added to obtain new LL band. The inverse DWT is applied on four bands to derive SR image. It is observed that the performance of the proposed method is better than existing methods.

Many image display applications and printing devices allow only limited number of colours. They have limited computational power and storage to produce high quality outputs on high bit-depth colour image. A dithering technique is called for here in order to improve the perceptual visual quality of the limited bit-depth images. A dithered image is represented by a natural colour in the low bit depth image colour for displaying and printing. This technique obtains low cost colour image in displaying the colour and printing image pixels. This study proposes the dithering technique based on Tchebichef Moment Transform (TMT) to produce high quality image at low-bit colour. Earlier, a 2×2 Discrete Wavelet Transform (DWT) has been proposed for better image quality on dithering. The 2×2 TMT has been chosen here since it performs better than the 2×2 DWT. TMT provides a compact support on 2×2 blocks. The result shows that 2×2 TMT gives perceptually better quality on colour image dithering in significantly efficient fashion.

The proliferation of digital communication has necessitated robust methods for secure data transmission.Audio steganography, the art of concealing secret information within audio signals, has emerged as a critical field for copyright protection and covert communication.This paper presents a comprehensive literature review of a multilayer security scheme that combines Advanced Encryption Standard (AES) with Wavelet Transform (Discrete Wavelet Transform -DWT, Lifting Wavelet Transform -LWT, or Integer Wavelet Transform -IWT).The proposed review focuses on a specific methodology where secret text is first encrypted using AES to ensure confidentiality and subsequently embedded into the wavelet-transformed coefficients of a cover audio file.This multilayer approach-cryptographic scrambling followed by steganographic embedding-aims to achieve high imperceptibility, robustness against signal processing attacks, and enhanced security.We analyze 15 significant papers, highlighting their methodologies, advantages, and limitations, to provide a roadmap for future research in high-security audio steganography.