Snehal Pawar, Amarsinh Deshmukh, Rahul Mulujkar
ENDOSCOPIC IMAGE ENHANCEMENT USING BLIND DENOISING *
Аннотация:
Biomedical images such as endoscopic images, retina, MRI, X-ray plays important role in the analysis and diagnosis of the internal body structure. Endoscopic image is used during pregnancy, plastic surgery, orthopedic surgery, spinal surgery etc. to examine internal body structure. Endoscopic images are corrupted with various types of noise. The noisy image results into inaccurate diagnosis and thus the endoscopic image denoising is essential. In this paper a method known as blind denoising has been used to improve the visual quality of the images. In the proposed method we first estimate the noise level in the image obtained. Now having known the noise level we apply BM3D algorithm to denoise the endoscopic image. By the proposed method it is found that the PSNR of the test image is improved. The enhanced image will help the doctors for accurate diagnosis
Ключевые слова:
blind denoising, noise level estimation, BM3D
I. INTRODUCTION Endoscopy is used to observe an internal body organ, structure or tissue. In this process long, thin tube is inserted into the body for diagnosis. The main application of endoscopy is imaging, minor surgery, diagnosis and so on. Few times in endoscopy due to internal bleeding and some other problems we cannot get a clear image. Thus the noisy image results into inaccurate diagnosis. So we need an engineering solution to this problem. Every real time image which is captured from the camera consists of some sort of noise. The noise may be from different types of source such as photon noise, thermal noise and quantization noise. Image denoising is important in many image processing applications and analysis. The study of image denoising started a few decades ago i.e. since 1970, but still we are lagging behind the mark of perfection. Image denoising is classified on different basis such as domain based approach, noise level based approach. According to the noise level based approach denoising is divided into two types non-blind denoising and blind denoising. This classification is based on whether the noise level is known or unknown. In case of non-blind denoising, the noise level(σn) is considered as known parameter, this is conventional way of denoising. On the other hand in case of blind denoising the noise level(σn) is unknown. We have to estimate the noise level parameter along with the denoising process. The accomplishment of image denoising algorithm predominantly depends upon the noise level (σn) estimation. In most of the commonly used noisy image model generally the noise is AWGN (Additive White Gaussian Noise). In the noise level estimation we mainly estimate the standard deviation (σn) for given single noise image. Lots of work is done on this topic, many algorithms [3-9] have been implemented. These algorithms are basically classified into three types of approaches i.e. filter based approach, patch based approach & statistical approach. Fig.1. Different types of Image Denoising In filter based approach [3],[5],[7] noisy image is passed through the high pass filter to get the suppressed image structure. Then the difference between the filtered image and the original image is considered as the noise. The problem with this denoising method is that the difference between the images is considered as noise, but this assumption is not always true especially in case of image with complex structure. In patch-based approaches [4], [7], [9], the image is divided into number of patches i.e. rectangular window of size N × N, and select the smooth patch among the separated patches. The smooth patch is selected on the basis of intensity level depending on the standard deviation. Here the consideration is the smooth patch consists of large amount of noise as compare to the true image contents of the patch. So approximate the true image contents to zero and hence by assuming the smooth patch consisting of only the noise, one can estimate the noise level. But the disadvantage of this method is that if the consideration goes wrong then overestimation or underestimation of the noise level takes place. II. NOISE LEVEL ESTIMATION Xinhao Liu [1] proposed a method for noise level estimation based on PCA. This method comes under patch based noise level estimation, here the input noisy image is divided into number of small patches in raster scan. Then we slide the window pixel by pixel so the patches are overlapped and the data model of the patches is represented as noisy image patch which is the combination of true image patch and noise. By taking the advantage of properties of the natural image i.e. the data of natural image spans only low dimensional sub space because of redundancy of natural image. If the data patch spans the subspace whose dimension is very less than the patches dimensions then that patch is known as low rank patches. Now here is the assumption that the minimum eigenvalue of the covariance matrix is equal to zero. The variance of the Gaussian noise is equally distributed in all the direction and all eigenvalues are same, so we can estimate the noise level. The main disadvantage of this method is that our assumption is not always true, especially in case of images with complex structure. So when the image with very fine details is given we can overestimate the noise level. To overcome this disadvantage we go for proposed method in which we choose the low rank patches. The low rank patches may consists of the patches with similar structure which includes the high frequency components like edges, corners or texture. A. Patch selection: There are many algorithms used for the patch selection depending on their applications. In an image patch local variance is an important parameter and it is useful to analyze the image structure as well as to select the image patch for noise level estimation. Lee and Popper [12] proposed an algorithm in which homogeneous patches are required to estimate the noise level, but here the homogeneous patches are known as the patches with small local variance. Similarly Pyatykh et al. [9], proposed an algorithm where he discarded the patches with large variances. The advantage of above two methods is that, both the algorithms are simple and fast but the major disadvantage is that it overestimates the noise level. To overcome the above disadvantage Shin et al.[7] proposed a method in which instead of selecting homogeneous patches or discarding the patches with large variance, he suggest to use the adaptive threshold of variance to select the patches. By using this method the performance is improved but not up to the mark. To deal with the above problem, Aishy Amer et al[4] proposed an algorithm in which high-pass operator as well as threshold is used to calculate the homogeneity measures, but the high pass operator is easily affected by the noise. Hence in case of high noise level estimation this method fails. So by analyzing above results we can conclude that noise level estimation using only the variance parameter is not accurate, rather we can say suitable patch selection is the first step for accurate noise level estimation and it depends not only on the image variance but also on the image structure. Zhu and Milanfar [13] concluded that image structure analysis can be done on the basis of gradient covariance matrix. Xinho Liu[1] proposed an algorithm for patch selection which is based on local image gradient matrix and its statistical properties to select low rank patches. The proposed algorithm for low rank patch selection is as follows
Номер журнала Вестник науки №7 (16) том 2
Ссылка для цитирования:
Snehal Pawar, Amarsinh Deshmukh, Rahul Mulujkar ENDOSCOPIC IMAGE ENHANCEMENT USING BLIND DENOISING // Вестник науки №7 (16) том 2. С. 86 - 96. 2019 г. ISSN 2712-8849 // Электронный ресурс: https://www.вестник-науки.рф/article/1949 (дата обращения: 19.04.2024 г.)
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