Gradient Histogram Preservation Research Articles

The AN IMAGE DENOISING ANALYSIS WITH ITERATIVE HISTOGRAM SPECIFICATION

In any application image denoising is a challenging task because noise removal will increase the digital quality of an image and will improve the perceptual visual quality. In spite of the great success of many denoising algorithms, they tend to smooth the fine scale image textures when removing noise, degrading the image visual quality. To address this problem, in this paper we propose a texture enhanced image denoising method by enforcing the gradient histogram of the denoised image to be close to a reference gradient histogram of the original image. Given the reference gradient histogram, a novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Simulation results show that the proposed method has given the better performance when compared to the existing algorithms in terms of peak signal to noise ratio (PSNR) and mean square error (MSE). To deal with this crisis, on this paper, we endorse a texture more desirable picture denoising process through implementing the gradient histogram of the denoised image to be just about a reference gradient histogram of the long-established snapshot. Given the reference gradient histogram, a novel gradient histogram renovation (GHP) algorithm is developed to enhance the texture buildings while casting off noise. Two neighborhood-founded editions of GHP are proposed for the denoising of pictures including areas with one-of-a-kind textures. An algorithm is also developed to conveniently estimate the reference gradient histogram from the noisy remark of the unknown snapshot. Our experimental outcome display that the proposed GHP algorithm can good retain the feel looks within the denoised graphics, making them appear more normal.

Hybrid image denoising based on region division

This paper proposes an efficient hybrid framework for image denoising, in which the advantages of different denoising methods are effectively incorporated by using the region prior knowledge. In detail, the input noisy image is first divided into a large number of overlapping patches followed by extraction of Speed Up Robust Feature (SURF), and then the noisy patches are classified into two categories based on Twin Support Vector Machine (TWSVM). The texture patches are enhanced via Gradient Histogram Preservation (GHP) while flat patches can be re-established using Block Matching Three-Dimensional Filtering (BM3D). Finally, the re-established images can be acquired by fusing the processing results of the two kinds of patches. To evaluate the effectiveness of the presented method, we conduct experiments on standard image datasets and compare the performance with other outstanding denoising approaches. Experimental results show that the presented method achieves better results, especially in containing textures and edges compared with existing image denoising methods.

Hybrid image denoising based on region division

This paper proposes an efficient hybrid framework for image denoising, in which the advantages of different denoising methods are effectively incorporated by using the region prior knowledge. In detail, the input noisy image is first divided into a large number of overlapping patches followed by extraction of Speed Up Robust Feature (SURF), and then the noisy patches are classified into two categories based on Twin Support Vector Machine (TWSVM). The texture patches are enhanced via Gradient Histogram Preservation (GHP) while flat patches can be re-established using Block Matching Three-Dimensional Filtering (BM3D). Finally, the re-established images can be acquired by fusing the processing results of the two kinds of patches. To evaluate the effectiveness of the presented method, we conduct experiments on standard image datasets and compare the performance with other outstanding denoising approaches. Experimental results show that the presented method achieves better results, especially in containing textures and edges compared with existing image denoising methods.

Optimized gradient histogram preservation with block wise SURE shrinkage for noise free image restoration

The image noise removal and restoration techniques invariably employ the hybrid filter and genetic algorithm approaches for recovery of noise free images. However, the desired level of denoising is not met with these approaches. The usage of adaptive genetic algorithm recovers the quality of the restored image. In order to improve the image denoising performance, an innovative noise removal method named optimized gradient histogram preservation (OGHP) is proposed. Initially, the preprocessing is applied on the noise contaminated image. Subsequently, the preprocessed image is subjected to OGHP noise exclusion procedure and stein’s unbiased risk estimate shrinkage. The resulted noiseless images are passed through the image restoration procedure carried out by employing the proposed adaptive genetic algorithm. The performance evaluation of the proposed method compared with the existing techniques demonstrates the efficiency of the proposed technique in noise elimination and effective restoration of image.

Efficient hybrid image denoising scheme based on SVM classification

An efficient hybrid image denoising method based on support vector machine (SVM) classification is designed and developed in this communication. Here, the input noisy image is first divided into large number of overlapping patches followed by extraction of local features from each patches using scale invariant feature transform (SIFT). Based on a predefined threshold, SVM is used to classify the patches into two classes such as texture patches and flat patches. The texture patches are processed through gradient histogram preservation (GHP) where flat patches are reconstructed using sparse based denoising method using the analysis, K-SVD. Finally, the reconstructed image is obtained by merging the results of the two denoising process. To find the efficiency of our proposed hybrid denoising scheme, we perform several experiments on some standard noisy images and compare the results with some state-of-the-art denoising methods Experiments indicate that the proposed hybrid scheme performs better denoising performance, particularly in preserving edges and textures as compared to existing denoising method.

Textured Enhanced Image De-noising using Fast Wavelet Transform

The digital imaging technologies possess very hasty development that uses Giga-pixels to store an image. Image de-noising algorithms plays a significant role in the restoration process. In an image the texture regions are homogeneous and are composed of local descriptor, a trade off exist between visual quality of image and the enhanced texture regions. In the existing paper the Gradient Histogram Preservation (GHP) method on the enhanced image regions have a limitation where it cannot be directly applied to nonadditive noise removal such as Multiplicative Poisson Noise (MPN) and Signal-Dependent Noise (SDN) to overcome the limitation Fast Wavelet Transform is used. In this work an image is first added with different types of noise like Additive White Gaussian Noise (AWGN), Salt and Pepper Noise, Poisson Noise, Signal Dependent noise and Flicker Noise, the noisy image is restored using filters, next the enhanced texture region of the image is chosen which is blurred or deformed and the fine details of the texture is obtained using Fast Wavelet Transform (FWT). The proposed work is analyzed in Frequency domain by considering various parameters like Peak Signal to Noise Ratio (PSNR), Correlation Factor (CF) and Standard Deviation (SD) and the quality of the enhanced region of the image is improved to the best level than the conventional noise removal algorithms.

Progressive Gradient Histogram Preservation Image Denoising

Image denoising has great significance in pre-processing step of imaging applications. Although state-of-the-art denoising methods are numerically notable and approach theoretical limits, they suffer from visible artifacts. The image denoising methods are transformed in both spatial and transformed frequency domain. Each domain has its advantages and shortcomings, which can be complemented by each other. We propose the Progressive gradient Histogram Preservation Image Denoising (PGHP) that combine both domains. This is a simple physical process, which progressively reduces noise by texture enhanced image denoising method of enforcing the gradient histogram preservation. The results with approx 1.08% improved are pointed out from the simulation.

An Improved Adaptive Regularization Method for Forward Looking Azimuth Super-Resolution of a Dual-Frequency Polarized Scatterometer

Dual-frequency polarized scatterometer (DFPSCAT) is a pencil-beam rotating scatterometer which is designed for snow water equivalent (SWE) measurement, and Doppler beam sharpening (DBS) technique is proposed for DFPSCAT to achieve the azimuth resolution. However, the DBS technique is inapplicable for the forward-looking and afterward-looking regions. Based on an approximate aperiodic model of scatterometer echo signal, an improved adaptive regularization deconvolution algorithm with gradient histogram preservation (GHP) constraint is implemented to settle the problem. To investigate its performance of resolution enhancement and resulted accuracy, both a synthetic backscattering coefficient ( $\sigma^{0}$ ) field reconstruction and SWE $\sigma^{0}$ reconstruction are carried out. The results show that the proposed method can recover the truth signal and achieve azimuth resolution of 2 km with the designed scatterometer system, which is required by the SWE retrieval. Moreover, the relative errors of reconstructed $\sigma^{0}$ are less than 0.5 dB that satisfy the accuracy requirement for SWE retrieval, and comparisons with observed results show that the error reduction is more than 0.03 dB. Meanwhile, a comparison between the proposed algorithm and some existing resolution enhancement methods is analyzed, which concludes that the proposed method can obtain a comparable resolution enhancement as $L_{1}$ method and has less noise. The technique is also verified with advanced scatterometer (ASCAT) scatterometer data.

Additive and Multiplicative Noise Removal by using Gradient Histogram Preservations Approach

denoising is a traditional yet essential issue in low level vision. Existing denoising technique denoise image but these techniques doesn't concern about multiplicative noise removals. Due to that image texture are not preserved and PSNR value does not properly improved. Image denoising technique uses a novel Gradient Histogram Preservation (GHP) algorithm which preserves image quality. Presently, this technique denoises only additive noise removal. It cannot be applied to non-additive removal, such as multiplicative, Poisson noise and signal- independent noise and it also takes more time in calculations. Since both the noises are dissimilar in nature therefore it is difficult to eliminate both the noises by using single filter. To solve the above issue ,in this paper a novel GHP approach is used to remove additive white Gaussian noise (AWGN) effectively. Since speckle noise is multiplicative in nature; it is converted into additive noise by logarithmic transformation method before apply GHP algorithm. In this paper we use the approach that is to acquire a logarithmic transformation, calculate a covariance matrix of the transformed data, generate random number which follows mean zero and variance/covariance c times the variance/covariance computed in the previous step, then take antilog of the normalized data and apply novel technique using, Fast Fourier Transfer (FFT), Gaussian filter, local content metrics texture ,Iterative Histogram Specifications (IHS) which can denoise both types of noise removal, additive and non-additive noise removal and also takes less calculation time.. In image processing FFT is used in a wide variety of applications, like image analysis, image reconstruction, image filtering and image compression . Gaussian separating is utilized to obscure pictures and evacuate clamor. The proposed algorithm offers to remove the multiplicative noise and improves the visual quality of images. Keywordsnoise, Texture, histogram specifications, sparse matrix representations, local content matrix

Image Super-Resolution Based on Structure-Modulated Sparse Representation

Sparse representation has recently attracted enormous interests in the field of image restoration. The conventional sparsity-based methods enforce sparse coding on small image patches with certain constraints. However, they neglected the characteristics of image structures both within the same scale and across the different scales for the image sparse representation. This drawback limits the modeling capability of sparsity-based super-resolution methods, especially for the recovery of the observed low-resolution images. In this paper, we propose a joint super-resolution framework of structure-modulated sparse representations to improve the performance of sparsity-based image super-resolution. The proposed algorithm formulates the constrained optimization problem for high-resolution image recovery. The multistep magnification scheme with the ridge regression is first used to exploit the multiscale redundancy for the initial estimation of the high-resolution image. Then, the gradient histogram preservation is incorporated as a regularization term in sparse modeling of the image super-resolution problem. Finally, the numerical solution is provided to solve the super-resolution problem of model parameter estimation and sparse representation. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed algorithm. Experimental results demonstrate that our proposed algorithm, which can recover more fine structures and details from an input low-resolution image, outperforms the state-of-the-art methods both subjectively and objectively in most cases.

Bilateral image denoising in the Laplacian subbands

Abstract This paper presents an image denoising algorithm, which applies bilateral filtering (BLF) in the Laplacian subbands. It is noted that the subband images have wider area of photometric similarity than the original, and hence, they can be more benefited by the BLF than the original. Specifically, an image is Gaussian filtered to obtain a low band image, and the low band image is subtracted from the original to have the high band signal, which forms the Laplacian subbands. For the high band image denoising, we derive an adaptive kernel that is dependent on the edge intensity and photometric similarity of subband images. The high band image is convolved with this kernel and then added to the denoised low band signal, which produces the denoised image. We also propose to process the denoised high band signal by the gradient histogram preservation method, for sharpening the edges with less noise amplification. Experimental results show that the proposed denoising method provides higher PSNR than the original BLF and other multi-resolution denoising algorithms. Since the high band image is also effectively denoised in this process, the sharpened image by high band modification is also visually more pleasing when compared with the results of the conventional sharpening methods.

Gradient histogram estimation and preservation for texture enhanced image denoising.

Natural image statistics plays an important role in image denoising, and various natural image priors, including gradient-based, sparse representation-based, and nonlocal self-similarity-based ones, have been widely studied and exploited for noise removal. In spite of the great success of many denoising algorithms, they tend to smooth the fine scale image textures when removing noise, degrading the image visual quality. To address this problem, in this paper, we propose a texture enhanced image denoising method by enforcing the gradient histogram of the denoised image to be close to a reference gradient histogram of the original image. Given the reference gradient histogram, a novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Two region-based variants of GHP are proposed for the denoising of images consisting of regions with different textures. An algorithm is also developed to effectively estimate the reference gradient histogram from the noisy observation of the unknown image. Our experimental results demonstrate that the proposed GHP algorithm can well preserve the texture appearance in the denoised images, making them look more natural.