# Methods for Improving Image Quality for Contour and Textures Analysis Using New Wavelet Methods
## 3 Results
Figure 4: Processing a degraded image with Gaussian additive white noise with the multiple Wiener filter in the wavelet domain. (**a**) Lena image, 512 \(\times\) 512 pixels, degraded, \(\sigma\) = 0.075, PSNR = 22.50 dB, C = 44.92%. (**b**) Image obtained by soft truncation with optimal threshold values, PSNR = 29.83 dB, C = 41.01%; this image is the estimate for Wiener filters. (**c**) Image obtained by processing with the empirical Wiener filter in the wavelet domain having as estimate the image from point b, PSNR = 30.08 dB, C = 41.67%. (**d**) Image obtained by processing with the multiple Wiener filter in the wavelet domain having as estimate the image from point b, PSNR = 31.50 dB, C = 45.15%. In addition to the need to improve imaging, medical applications also need these applications to run in real time and this necessity was the basis for the design of the method described in the article, based on the modified Wiener filter and nonlinear cellular network. The proposed method for improving images with modified Wiener filter and nonlinear cellular network can be applied with success also with low contrast CT (computer  
Figure 5: Original image with noise: (**a–d**) local histograms. Figure 6: Image obtained using Wiener filter and cellular nonlinear network: (**a,b**) local histograms for \(\lambda=1\); (**c**) global histogram. The effect of preprocessing an image by applying the method described above can be highlighted by evaluating local histograms from the same interest areas of the initial image (Figure 5), respectively, from the image obtained by using the AB template for \(\lambda=1\) (Figure 6). Nonlinear cellular network processing based on the described method achieves a local balancing of the intensity level. As can be seen, the proposed method provides robust results in eliminating noise from images, as well as improving image contrast and contour detection. The method implemented has the advantage of a small computation time because the nonlinear cellular network used has a single layer, and the operators used are of size \(3\times 3\), which allows the calculations to be performed in a single step. The method can be extended to more complex processing that can be described by differential equations, considering the relations \((26,27)\div(32)\). Following the simulations performed, it is found that both methods lead to significant improvements both in terms of the amount of noise removed from the image, but also in terms of the quality of the contours in the processed images. At the same time, the combined use of the two methods that are the subject of this article is tested, the results obtained being superior to those obtained by using each of them. The interest in such filters is determined by the fact that much of the existing information in the images is provided by contours, and most of the noise removal methods cause them to fade. In the present case, at the cost of removing a smaller amount of noise in the contour-containing regions, it can be better preserved, as shown by the results shown in Figures 7-9. There is thus a trade-off between the amount of noise removed and the quality of preserving the contours in the image. It should be noted that in the results presented in Figures 8 and 9, the noise reduction is accompanied by an improvement of the contours of the processed image, leading to a microscopic analysis, highlighting very well the contours of tumor formations from diseased tissues to healthy tissues. Such an estimate is useful for the medical act, especially for surgery. Additionally, the proposed method, in addition to the contour enhancement property, highlights the deconvolution applications for removing or reducing the blurring of energy areas in images by highlighting their vascularization elements (Figures 8 and 9). Imaging techniques are especially useful for interpreting biomedical CT/MRI images.