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This review paper presents various techniques applied in satellite image enhancement and restoration. Our review findings shows that there exists lot of scope for performing satellite image enhancement and restoration using amalgamating soft computing techniques and conventional image processing.
ISSN:2249-5789 S Maheshwari et al, International Journal of Computer Science & Communication Networks,Vol 6(4),198-204 Satellite Image Enhancement and Restoration – A Review Mrs.S.Maheshwari, 2Dr.P.Krishnapriya Assistant Professor, PG & Research Department of Computer Science Dr.N.G.P Arts and Science College, Coimbatore-48 sivamahesh@gmail.com Director - Department of Computer Applications, CIMAT, Coimbatore, India, pkpriyaa@yahoo.com Abstract Satellite image processing is one of the thrust areas in the field of computer science research Images taken by satellites possibly degraded due to climate, weather and other factors Satellite image enhancement and restoration is scientifically possible by applying image processing and other soft computing techniques This review paper presents various techniques applied in satellite image enhancement and restoration Our review findings shows that there exists lot of scope for performing satellite image enhancement and restoration using amalgamating soft computing techniques and conventional image processing The proposed doctoral research work is descriptively portrayed in the paper Keywords: Satellite imagery, satellite image processing, enhancement, restoration, fusion Introduction The purpose of image enhancement and restoration techniques is to perk up a quality and feature of a satellite image that result in improved image than the original one There exist so many image enhancement algorithms in the literatures that most often IJCSCN | August-September 2016 Available online@www.ijcscn.com used techniques as global histogram equalization or general histogram equalization [1] Usually histogram equalization technique alters the intensity histogram in order to fairly accurate a uniform distribution The major pitfall of this histogram equalization technique is the problem of covering global image properties that may not be appropriately applied in a local context [2] It is noteworthy that histogram modification concerns with all regions of the image equally that will result in degraded local realization in terms of detail conservation In the same manner, various local image enhancement algorithms are also seen in the literatures Enhancement is a key step in the field of satellite image processing The assorted noises and relics in imaging roles mortify the quality of the satellite image This paper reviews recent literatures on satellite image enhancement and restoration Related Works Bhutada et al proposed [3] a novel approach which utilizes features of wavelet and curvelet transform, separately and adaptively, in ‘homogeneous’, ‘non-homogeneous’ and ‘neither 198 ISSN:2249-5789 S Maheshwari et al, International Journal of Computer Science & Communication Networks,Vol 6(4),198-204 homogeneous nor non-homogeneous’ regions, which are identified by variance approach The edgy information that could not be retained by wavelet approach is extracted back from its residue by denoising it with curvelet transform This extracted information is used as edge structure information (ESI) for fusing offshore regions of denoised images obtained by usage of wavelet and curvelet transform The result of the image enhanced by such spatially adaptive fusion technique shows the improvement in the preservation of the edgy information It also yields better smoothness in background (homogeneous region or non-edgy region) due to the removal of fuzzy edges developed during the denoising process by the curvelet transform Yun Ling et al [4] have presented an adaptive tone-preserved algorithm for image detail enhancement in order to retain the tonal distribution of the input image and avoid experiential manipulation Initially, domain transform based multi-scale image decomposition is carried out to quickly divide the input image into a base image which contains the coarse-scale image information, and the detail layers which contain the fine-scale details Then, during the process of detail enhancement and synthesis, the authors constructed an adaptive detail enhancement function based on the edge response, to prevent the exaggeration of strong edges and increase the enhancing magnitude of small details Finally, in order to keep the color values of the input image and the gradient values of the detail enhanced image, a tonal correction algorithm based on energy optimization is presented to eliminate the distinct tonal IJCSCN | August-September 2016 Available online@www.ijcscn.com differences of the enhanced image from the input image Their experimental results show that tone-consistent image detail enhancement effect is available for arbitrary input images with unified parameters setting, which is superior to the state-of-the-art methods In the study conducted by Kumar et al [5], an improved multi-band satellite contrast enhancement technique based on the singular value decomposition (SVD) and discrete cosine transform (DCT) was proposed for the feature extraction of low-contrast satellite images using normalized difference vegetation index (NDVI) technique Their method employs multi-spectral remote sensing data technique to find the spectral signature of different objects such as the vegetation index and land cover classification presented in the satellite image Their proposed technique converts the image into the SVD-DCT domain and after normalizing the singular value matrix; the enhanced image is reconstructed by using inverse DCT The visual and quantitative results included in this study clearly show the increased efficiency and flexibility of the proposed method over the existing methods Their simulation results showed that the enhancement-based NDVI using DCT-SVD technique is highly useful to detect the surface features of the visible area which are extremely beneficial for municipal planning and management In [6] Bhandari et al have presented wavelet filter based low contrast multispectral remote sensing image enhancement by using singular value decomposition (SVD) The input image is decomposed into the four frequency 199 ISSN:2249-5789 S Maheshwari et al, International Journal of Computer Science & Communication Networks,Vol 6(4),198-204 subbands through discrete wavelet transform (DWT), and estimates the singular value matrix of the low-low subband image and then, it reconstructs the enhanced image by applying inverse DWT Their technique is especially useful for enhancement of INSAT as well as LANDSAT satellite images for better feature extraction The singular value matrix represents the intensity information of the given image, and any change on the singular values changes the intensity of the input image Their proposed technique converts the image into DWT-SVD domain and after normalizing the singular value matrix; the enhanced image is reconstructed with the help of IDWT The visual and quantitative results clearly show the edge sharpness, increased efficiency and flexibility of the proposed method based on Meyer wavelet and SVD over the various wavelet filters and also with exiting GHE technique Their experimental results (Mean, Standard Deviation, MSE and PSNR) derived from Meyer wavelet and SVD show the superiority of the proposed method over conventional methods Arici et al [7] proposed a general framework based on histogram equalization for image contrast enhancement In their framework, contrast enhancement is posed as an optimization problem that minimizes a cost function The authors also stated that by introducing specifically designed penalty terms, the level of contrast enhancement can be adjusted; noise robustness, white/black stretching and mean-brightness preservation may easily be incorporated into the optimization Analytic solutions for some of the important criteria are presented Finally, a low-complexity algorithm for contrast IJCSCN | August-September 2016 Available online@www.ijcscn.com enhancement is presented Bhandari et al [8] also proposed a novel contrast enhancement technique for contrast enhancement of a low-contrast satellite image based on the singular value decomposition (SVD) and discrete cosine transform (DCT) The singular value matrix represents the intensity information of the given image and any change on the singular values change the intensity of the input image Their proposed technique converts the image into the SVD-DCT domain and after normalizing the singular value matrix; the enhanced image is reconstructed by using inverse DCT Their visual and quantitative results suggested that their proposed SVD-DCT method clearly shows the increased efficiency and flexibility of their proposed method over the exiting methods such as the histogram equalization, gamma correction and SVD-DWT based techniques In the process of satellite imaging, the observed image is blurred by optical system and atmospheric effects and corrupted by additive noise The image restoration method known as Wiener deconvolution intervenes to estimate from the degraded image an image as close as possible to the original image The effectiveness of this method obviously depends on the regularization term which requires a priori knowledge of the power spectral density of the original image that is rarely, if ever, accessible, hence the estimation of approximate values can affect the restored image quality In [9] Aouinti et al came up with the idea consisted of applying the genetic approach to the Wiener deconvolution for satellite image restoration through the optimization of this regularization term in order to achieve the 200 ISSN:2249-5789 S Maheshwari et al, International Journal of Computer Science & Communication Networks,Vol 6(4),198-204 best possible result Sajid and Khurshid [10] proposed Recursive Least Square (RLS) adaptive algorithm which is used for image restoration from highly noise corrupted images The implementation of their proposed methodology is being carried out by estimating the noise patterns of wireless channel through configuring System Identification with RLS adaptive algorithm Then, these estimated noise patterns are eliminated by configuring Signal Enhancement with RLS algorithm The restored images are functioned for further denoising and enhancement techniques Performance is evaluated by means of Human Visual System, quantitative measures in terms of MSE, RMSE, SNR & PSNR and by graphical measures Their experimental results demonstrated that RLS adaptive algorithm efficiently eliminated noise from distorted images and delivered a virtuous evaluation without abundant degradation in performance Zhang and Man [11] have proposed a satellite image adaptive restoration method which avoids ringing artifacts at the image boundary and retains oriented features Their method combines periodic plus smooth image decomposition with complex wavelet packet transforms The framework first decomposes a degraded satellite image into the sum of a “periodic component” and a “smooth component” The Bayesian method is then used to estimate the modulation transfer function degradation parameters and the noise The periodic component is deconvoluted using complex wavelet packet transforms with the deconvolution result of the periodic component then combined with IJCSCN | August-September 2016 Available online@www.ijcscn.com the smooth component to get the final recovered result Their test results showed that their strategy effectively avoids ringing artifacts while preserving local image details Thriveni and Ramashri [12] proposed a DWT-PCA based fusion and Morphological gradient for enhancement of Satellite images The input image is decomposed into different sub bands through DWT PCA based fusion is apply on the low-low sub band, and input image for contrast enhancement IDWT is used to reconstructs the enhanced image To achieve sharper boundary discontinuities of image, an intermediate stage estimating the fine detail sub bands is required This has been done by the success of threshold decomposition, morphological gradient based operators are used to detect the locations of the edges and sharpen the detected edges Their proposed method has been shown that improved visibility and perceptibility of various digital satellite images Aedla [13] et al have presented a new contrast enhancement technique for satellite images based on clipping or plateau histogram equalization Their technique adopted Bi-Histogram Equalization with Plateau Limit (BHEPL) for image decomposition and Self-Adaptive Plateau Histogram Equalization (SAPHE) for threshold calculation Their proposed method has been compared with existing methods such as Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Dynamic Histogram Equalization (DHE), Bi-Histogram 201 ISSN:2249-5789 S Maheshwari et al, International Journal of Computer Science & Communication Networks,Vol 6(4),198-204 Equalization with Plateau Limit (BHEPL) and Self-Adaptive Plateau Histogram Equalization (SAPHE) with image quality measures such as Absolute Mean Brightness Error (AMBE) and Peak-Signal to Noise Ratio (PSNR) inverse DWT Their technique is applied to grey level, colour image and satellite image and their comparative analysis were done Their experimental results showed the superiority of their proposed method over conventional techniques Soni et al [14] proposed an improved method based on evolutionary algorithms for denoising of satellite images In their approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC), and particle swarm optimisation (PSO) technique and their different variants are exploited for learning the parameters of adaptive thresholding function required for optimum performance It was found that the CS algorithm and ABC algorithm-based denoising approach gave better performance in terms of edge preservation index or edge keeping index (EPI or EKI) peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) as compared to PSO-based denoising approach Their proposed technique has been tested on satellite images The quantitative (EPI, PSNR and SNR) and visual (denoised images) results show superiority of the proposed technique over conventional and state-of-the-art image denoising techniques Proposed Research Work Bidwai and Tuptewar [15] have developed a method to enhance the quality of image The enhancement is done both with respect to resolution as well as contrast Their proposed technique uses DWT and SVD Their technique decomposes the input image into four sub-bands by using DWT and estimates singular value matrix of low frequency sub-band image, then it reconstructs enhanced image by applying IJCSCN | August-September 2016 Available online@www.ijcscn.com 3.1 Phase - In this initial phase we aim to remove noise from image using enhanced filtering technique The proposed enhanced filter is applied to the satellite images that are affected by Gaussian noise and the filter is applied images affected by impulse noise (salt and pepper noise) Using the proposed filtering algorithm, the Gaussian noise is removed and the satellite image is input to the p (i, j) Then an amalgam filter is developed by combining two filters (adaptive median filter and p (i, j) that are affected by Gaussian noise, and a noise free image is undergone the restoration process The image I noise is removed and a noise-free output image is obtained Here, the bilateral filter performance is improved by optimizing the two parameters that control the filter behavior p(i, j) affected by the impulse noise is denoised by applying the optimized bilateral filter 3.2 Phase - This phase is aimed to develop a method to extract unique features from satellite image based on Hough Transformation and Local Binary Patterns The Hough Transformation method is employed in order to highlight linear features It is assumed, however, that applying this method by using an LBP operator that supports the features of the 202 ISSN:2249-5789 S Maheshwari et al, International Journal of Computer Science & Communication Networks,Vol 6(4),198-204 texture associated with rectitude will improve results The contribution of this work is the LBP texture operator that introduces criteria, added to the image radiometry to improve extraction This ensures convergence to an optimal solution while controlling the contextual information The performance of this method will be verified using satellite images It is to be noted that proposed system is aimed to be an effective proposal for application to satellite images at medium and high resolution DWT-PCA based fusion and morphological gradient, periodic plus smooth image decomposition and complex wavelet packet transforms, RLS adaptive filter and enhancement, genetic approach to the Wiener deconvolution, discrete cosine transform and SVD were reviewed Our findings shows that there exists lot of scope for performing satellite image enhancement and restoration using amalgamating soft computing techniques and conventional image processing 3.3 Phase - In this phase of research, quality of the satellite image is aimed to be improved where criteria including removal of illumination color cast and information content increment are to be met Genetic algorithm is chosen for accomplishing this goal In GA the Pareto front concept is adopted that converts the conventional GA into the multi-objective genetic algorithm (MOGA) The proposed MOGA for image enhancement contains several components that includes initialization, iteration with objective evaluation, determination of the Pareto front and its management, update In particular the color correction process and the assignment of the qualities in color correction along with information gain are given priority Conclusions In this paper several mechanisms which includes improved sub-band adaptive Thresholding function based on evolutionary algorithms, Satellite image contrast enhancement algorithm based on plateau histogram equalization, edge preserving Satellite image enhancement using IJCSCN | August-September 2016 Available online@www.ijcscn.com References [1] Gonzalez RC, Woods RE “Digital Image Processing” 3rd ed Englewood Cliffs, NJ: Prentice-Hall; 2007 [2] Tang J, Peli E, Acton S, “Image enhancement using a contrast measure in the compressed domain”, IEEE Signal Processing Letters Vol.10, No.4, 2003, pp.289–92 [3] G.G.Bhutada, R.S.Anand, S.C.Saxena, "Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform", Digital Signal Processing, Volume 21, Issue 1, January 2011, pp.118-130 [4] Yun Ling, Caiping Yan, Chunxiao Liu , Xun Wang, Hong Li, "Adaptive tone-preserved image detail enhancement", The Visual Computer, Volume 28, Issue 6, June 2012, pp.733-742 [5] A.Kumar, A.K.Bhandari, P.Padhy, "Improved normalized difference vegetation index method based on discrete cosine transform 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gradient for enhancement of Satellite images The input image is decomposed into different sub bands... Islamabad, 2015, pp 1-7 [11] Y Zhang and Y Man, "Satellite image adaptive restoration using periodic plus smooth image decomposition and complex wavelet packet transforms," in Tsinghua Science and