Nonlocal means (NLM) which utilizes the self-similarity is considered as one of the most popular denoising techniques. Although NLM can attain significant performance, it shows a few loopholes, such as its computational complexity when it comes to similarity measurements, and the small number of sufficient candidates that use to choose the target patches which have complicated textures. In this paper, the use of clustering based on moment invariants and the hidden Markov model (HMM) is proposed to achieve preclassification and thus capture the dependency between additive white Gaussian noise pixel and its neighbors on the wavelet transform. The HMM also allows hidden states to connect to one another to capture the dependencies among coefficients in the transform domain. In the practical part, the experimental results present that the proposed algorithm has the ability to show denoised images better than the results of state-of-the-art denoising methods both objectively in peak signal-to-noise ratio and structural similarity and subjectively using visual results, especially when the noise level is high.

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This study proposes an automatic noise estimation method based on local statistics for additive white Gaussian noise. Noise estimation is an important process in digital imaging systems. For example, the performance of an image denoising algorithm can be significantly degraded because of poor noise level estimation. Most of the literature on the subject tends to use the true noise level of a noisy image when suppressing noise artifacts. Moreover, even with the given true noise level, these denoising techniques still cannot attain the best result, particularly for images with complicated details. In this study, a patch-based estimation technique is used to estimate for noise level and applies it to the proposed blind image denoising algorithm. Our approach includes selecting low-rank sub-image with removing high-frequency components from the contaminated image. This selection is according to the gradients of patches with the same statistics. Consequently, we need to estimate the noise level from the selected patches using principal component analysis (PCA). For blind denoising applications, the proposed denoising algorithm integrates the undecimated waveletbased denoising algorithms and PCA to develop the subjective and objective qualities of the observed image, which result from filtering processes. Experiment results depict that the suggested algorithm performs efficiently over a wide range of visual contents and noise conditions, as well as in additive noise. Associated with different conventional noise estimators, the proposed algorithm yields the best performance, higher-quality images, and faster running speed.

The recognition of plants is directly associated to society's life. Leaves from plants are proved to be a feasible source of information used to identify plant species. The recognition system of leaves is accomplished automatically using the experts of human being. Unfortunately, it has their loopholes that are a time consuming processes and low-effectiveness progression. The leaves classification using predictable process is quite complicated, time complexity, and as a result of using very long-termed in botanical science for non-experts that make it more irritated operation. Thus, the prompt developments in digital images, computer vision and object detection and recognition systems encourage scientists to work towards plant species recognition according to image processing technology. In this study, an image processing algorithm in order to find out the shape structure of tested plants is presented. This technique exploits the variant to scaling shift, spin technique, scaling approach, and filtering processes. The leaf contours of the same plants are computed using Support Victor Machine (SVM) where the similar sequences of the same contours usually carry the same features while the different plants sequences have different contours. In this regard, SVM classifier is exploited to be applied as a classifier to the plant's leaf. In the Experiment part, the finding was taken from Flavia dataset and it demonstrated that the suggested technique has high recognition efficiency compared to state of the art methods and is shows better quality images especially in complicated features of digital images such as ridges, edges, lines, curves and complicated contours.

Digital images play an essential role in analysis tasks that can be applied in various knowledge domains, including medicine, meteorology, geology, and biology. Such images can be degraded by noise during the process of acquisition, transmission, storage, or compression. The use of local filters in image restoration may generate artifacts when these filters are not well adapted to the image content as a result of the heuristic optimization of local filters. Denoising methods based on learning procedure are more capable than parametric filters for addressing the conflicts between noise suppression and artifact reduction. In this study, we present a nonlinear filtering method based on a two-step switching scheme to remove both salt-and-pepper and additive white Gaussian noises. In the switching scheme, two cascaded detectors are used to detect noise, and two corresponding estimators are employed to effectively and efficiently filter the noise in an image. In the process of training, a method according to patch clustering is utilized, and genetic programming (GP) is subsequently applied to determine the optimum filter (wavelet-domain filter) for each individual cluster, while B Asem Khmag in testing part, the optimum filter trained beforehand by GP is recovered and used on the inputted corrupted patch. This adaptive structure is employed to cope with several noise types. Experimental and comparative analysis results show that the denoising performance of the proposed method is superior to that of existing denoising methods as per both quantitative and qualitative assessments.

In this paper, an automatic estimation of additive white Gaussian noise technique is proposed. This technique is b uilt according to the local statistics of Gaussian noise. In the field of digital signal processing, estimation of the noise is considered as pivotal process that many signal processing tasks relies on. The main aim of this paper is to design a patch-b ased estimation technique in order to estimate the noise level in natural images and use it in b lind image removal technique. The estimation processes is utilized selected patches which is most contaminated sub -pixels in the tested images sing principal component analysis (PCA). The performance of the suggested noise level estimation technique is shown its superior to state of the art noise estimation and noise removal algorithms, the proposed algorithm produces the b est performance in most cases compared with the investigated techniques in terms of PSNR, IQI and the visual perception.

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