# Methods for Improving Image Quality for Contour and Textures Analysis Using New Wavelet Methods
## 1 Introduction
[PERSON], [PERSON] and [PERSON] (2019) are analyzing how increasing the contrast of a picture makes it easier to analyze. The noise that corrupts the image of magnetic resonance imaging (MRI) is coming from the acquisition's moment or the transmission process. Seven algorithms have been compared on their effectiveness on MRI images. [PERSON] and [PERSON]. (2019) are using the contourlet transform algorithm to analyze the magnetic resonance imaging denoising. The algorithm has shown that this algorithm obtains higher PSNR (peak signal to noise ratio) compared with the wavelet algorithms in the presence of additive white Gaussian noise (AWGN) on MRI. [PERSON] (2019) are using the contourlet transform using directional and multiscale filter banks. This algorithm contains time--frequency--localization and multiscale and high degree directionality. [PERSON] and [PERSON]. [29] are highlighting the importance of the correct reading of medical images. This research's purpose is to use the threshold values in a compression algorithm, which is expected to decrease the image size. [PERSON], [PERSON], [PERSON] and [PERSON] [30] are analyzing the denoising using computed tomography image that is dependable on image quality. The used method has three stages of processing. In conclusion, the multiwavelet based denoising technique can be used to denoise and obtain quality CT images. [PERSON] et al. [31] are proposing a super resolution (SR) method to recover high resolution (HR) medical images. The method is coupled with dual-tree complex wavelet transform (DTCWT) and modeled and applied on multiple medical images. The solution proved to be more qualitative and quantitative compared with other methods. [PERSON], [PERSON] et al. [32] are proposing the denoising of astronomical images named local sparse representation (LSR) using iterative shrinkage-thresholding algorithm (ISTA). Using the optimization method, they were able to update the dictionary and the sparse coding vectors and to prove the superiority of the method. [PERSON] and [PERSON] [33] are working to provide high quality images eliminating multiple types of noises, like Brownian noise, speckle noise, pepper noise and the Gaussian noise. Applying the appropriate algorithm to the specific type of noise is a very important element. They are coupling the type of noise with the best solution, for example for the corruption with salt and paper noise the best method to apply is the filtering. [PERSON] et al. [34] are approaching the images from the degradation of the structural information view. To extend the limitation of the error sensitivity is used as an alternative to the structural similarity as the structural similarity index measure (SSIM) index is developed and proves the validity of the method. They are investigating multiple issues and try to identify the appropriate solutions for it. [PERSON]; [PERSON] and [PERSON] [35] are considering the quality of the images from medical science, satellite or high-definition television (HDTV). They propose a method using non-subsampled contourlet transform (NSCT) based learning and an interior-point barrier (IBP) to optimize the cost function. They have undertaken multiple simulations and proved the validity of the method. [PERSON] et al. [36] tested multiple parametric models and calibrated them. Using data records and default values, the tuned models they built model evaluation and parametrization. The results are presenting that the errors are reduced. [PERSON], [PERSON], [PERSON], and [PERSON] [37] are approaching the noised images and identify the customized wavelet filters, specific for the type of noise, using simulated annealing (SA). Compared with the VisuShrink image the method they developed shows better experimental results, the VisuShrink is outperformed in all the experiments. [PERSON] et al. [38] are proposing two solutions for image denoising, a convolutional neural network and a deep neural network. There are two tasks presented, to denoise images and use deep learning for high level vision tasks.