Satellite Image Processing Using Discrete Cosine Transform and Singular Value Decomposition

A Modified DWT-SVD Algorithm for T1-w Brain MR Images Contrast Enhancement

In this article, a new contrast enhancement approach is presented for quality enhancement of low-contrast satellite images. The proposed technique is based on the Artificial Bee Colony (ABC) algorithm using Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD). The method employs the ABC technique to learn the parameters of the adaptive thresholding function required for optimum enhancement. In this approach, the input image is primarily decomposed into four sub-bands through DWT, and then each sub-band of DWT is optimized through the ABC algorithm. After that, a singular value matrix of the low–low thresholded sub-band image is estimated and, finally, the enhanced image is constructed by applying inverse DWT. The results obtained through this method reveal that the proposed methodology gives better performance in terms of peak signal-to-noise ratio (PSNR), mean square error (MSE), and mean and standard deviation as compared to General Histogram Equalization (GHE), Discrete Cosine Transform and Singular Value Decomposition (DCT-SVD), DWT-SVD, Particle Swarm Optimization (PSO), and modified versions of the PSO-based enhancement approach.

A contrast enhancement technique using scaling of internal noise of a dark image in discrete cosine transform (DCT) domain has been proposed in this paper. The mechanism of enhancement is attributed to noise-induced transition of DCT coefficients from a poor state to an enhanced state. This transition is effected by the internal noise present due to lack of sufficient illumination and can be modeled by a general bistable system exhibiting dynamic stochastic resonance. The proposed technique adopts a local adaptive processing and significantly enhances the image contrast and color information while ascertaining good perceptual quality. When compared with the existing enhancement techniques such as adaptive histogram equalization, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, multi-contrast enhancement, multi-contrast enhancement with dynamic range compression, color enhancement by scaling, edge-preserving multi-scale decomposition and automatic controls of popular imaging tool, the proposed technique gives remarkable performance in terms of relative contrast enhancement, colorfulness and visual quality of enhanced image.

In this work, we have proposed a new image contrast enhancement technique based on complex wavelet transform (CWT) and singular value decomposition (SVD). The technique decomposes the input image into the eight frequency subbands by using CWT and estimates the singular value matrix of the real and complex low-low subbands, and then it reconstructs the enhanced image by applying the inverse CWT (ICWT). The technique is compared with the conventional image equalization techniques such as standard general histogram equalization (GHE) and local histogram equalization (LHE), as well as state-of-art technique such as Brightness Preserving Dynamic Histogram Equalization (BPDHE) and singular value equalization (SVE). The experimental results are showing the superiority of the proposed method over the conventional and the state-of-art techniques.

In this paper, a new method has been proposed for low contrast image enhancement based on the lifting discrete wavelet transform and singular value decomposition. The given input image decomposes in four subbands with lifting discrete wavelet transform scheme. Then, we estimate the singular value matrix of the low-low subband image and by applying inverse lifting wavelet transform reconstruct enhanced image. The proposed method also compared with the existing traditional methods like general histogram equalization, local histogram equalization and dynamic histogram equalization. The method is tested on different images. The results show that proposed method is batter then existing methods.

One of the most important preprocessing techniques is image Contrast Enhancement which is a technique that improve visual quality of an input image becoming more suitable for human analysis and perception. Numerous researches have been already developed for enhancement of Medical image for their important application, Traditional Gamma Correction is found to be one of the simplest technique for the contrast enhancement of medical image. This technique uses a set of varying parameter (y, c) which adjust effectively gray value intensity of the input image. Otherwise, Adaptive Gamma Correction technique have been appeared and have been proved its effectiveness by the use of adaptive (y, c) parameter which are determined adaptively from statistical information of input image. Traditional Gamma Correction (TGC) and Adaptive Gamma Correction (AGC) have been applied on three brain MRI modalities for patient with Multiple Sclerosis pathology. Qualitative and quantitative results are presented to illustrate the comparison of TGC and AGC to enhance the contrast of MRI images for better appearance of normal tissue and diseased tissue affected by MS pathology.

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.

Enhancement of mammogram images against low-contrast and poor illumination is still a challenge for researchers. Focusing on such issues, this manuscript presents a two-stage enhancement technique for mammogram images. The first stage of the image enhancement deals with illumination control using the corrective-adaptive gamma correction (CAGC) approach. In the second stage, contrast enhancement operation on the luminosity-controlled image is incorporated. In order to enhance visual perception against low-contrast, the combined application of discrete wavelet transform (DWT) and singular value decomposition (SVD) is incorporated. The experimentation of the proposed technique was performed over a publicly available mini-MIAS dataset. The proposed technique is evaluated on various quantitative parameters such as Pearson correlation coefficient (PCC), universal image quality index (IQI), structural similarity index measurement (SSIM), contrast improvement index (CII), average mean brightness error (AMBE), and mean absolute error (MAE) and obtain the average values of 0.996, 0.912, 0.921, 1.098, 15.732, and 15.624 that are promising results as compared to the other traditional methods. This study also compares the proposed technique with state-of-art methods and achieves better performance, resulting in significant improvement in contrast enhancement and local information preservation of mammogram images.

The paper presents a new digital image hiding algorithm based on wavelet packets transform and singular value decomposition. The low-frequency sub-band of wavelet packets transform has strong anti-jamming capacity and the singular value has very strong stability. The presented algorithm implements bit plane decomposition on the secret image and wavelet packet decomposition on the carrier image. Then, it hides the bit planes with important information into the singular value matrix of the low frequency coefficient matrix, and also hides the bit planes with secondary information into the remainder sub-band matrix with higher entropy energy. The hiding location is adaptively determined by the carrier image. The experimental results indicate that, the proposed image hiding algorithm has strong robustness and anti-attack, and it also has good invisibility and big capability.

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 presents a new robust and invisible blind watermarking approach of colour images for copyright protection in hybrid DWT-DCT-SVD domain. In the proposed method, firstly the luminance component (Y) of the cover image is decomposed up to one level of discrete wavelet transform (DWT) coefficients and the low frequency band (LL) is transformed by discrete cosine transform (DCT). Then several selected low and intermediate frequency DCT coefficients of each block are extracted to generate a feature matrix and singular value decomposition (SVD) transform is applied to the feature matrix. Finally the watermark information is embedded by modifying the singular values of the feature matrix. Experimental results demonstrate that the proposed approach outperforms some popular existing watermarking methods in robustness against median filtering, Gaussian filtering, salt and pepper noise, average filtering, Gaussian noise, histogram equalisation, and so on, especially in case of lossy JPEG compression in addition to good imperceptibility.

This paper presents an audio watermarking scheme that ensures the efficient of robustness-distortion ratio and a pleasing amount of capacity. Quantization Index Modulation (QIM) method synergism along with Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), and Cartesian to Polar Transformation (CPT) to emphasize copyright protection for harmonical work of art. DWT synergism along with DCT, SVD, and CPT to emphasize copyright protection for harmonical work of art. The system establishes with DWT that divides the signal within a specific range of frequency, alter it to the frequency domain by DCT, partite with mathematical processing SVD, and transform the peculiar value of matrix-S to polar value using CPT, furthermore, QIM embed the watermark into the angular value from the CPT. The system performance is astonishing, tested using 4 different audio host signal and evaluated considers to desirable Bit Error Rate (BER) <; 0.025, Signal-to-Noise Ratio (SNR)>21dB, a great amount of data payload that is 525 bit per second, and the noise caused by watermark is inaudible. All of the experiments, trials, and results show that the designed audio watermarking system is robust and imperceptible.

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.

Early detection of breast cancer in the mammograms is very essential in the field of medicine. Contrast enhancement for the detection of micro calcification of mammograms based on the Histogram Modified Contrast Limited Adaptive Histogram Equalization (HM-CLAHE) is presented. Histogram equalization is an effective and simple technique for contrast enhancement. The standard histogram equalization (HE) usually results in excessive contrast enhancement because of lack of control on the level of enhancement. The Histogram Modified Contrast Limited Adaptive Histogram Equalization (HM CLAHE) is proposed in this paper to adjust the level of contrast enhancement, which in turn gives the resultant image a strong contrast and brings the local details for more relevant interpretation. It incorporates both histogram modifications as an optimization technique and Contrast Limited Adaptive Histogram Equalization. This method is tested for Mias mammogram images. The performance of this method is determined using the parameter like Enhancement Measure (EME). From the subjective and quantitative measures it is interesting that this proposed technique provides better contrast enhancement with preserving the local information of the mammogram images.

Magnetic resonance imaging (MRI) is a real assistant for doctors. It provides rich information about anatomy of human body for precise diagnosis of a diseases or disorder. But it is quite challenging to extract relevant information from low contrast and poor quality MRI images. Poor visual interpretation is a hindrance in correct diagnosis of a disease. This creates a strong need for contrast enhancement of MRI images. Study of existing literature shows that conventional techniques focus on intensity histogram equalization. These techniques face the problems of over enhancement, noise and unwanted artifacts. Moreover, these are incapable to yield the maximum entropy and brightness preservation. Thus ineffective in diagnosis of a defect/disease such as tumor. This motivates the authors to propose the contrast enhancement model namely optimized double threshold weighted constrained histogram equalization. The model is a pipelined approach that incorporates Otsu's double threshold method, particle swarm optimized weighted constrained model, histogram equalization, adaptive gamma correction, and Wiener filtering. This algorithm preserves all essential information recorded in an image by automatically selecting an appropriate value of threshold for image segmentation. The proposed model is effective in detecting tumor from enhanced MRI images.