## 1 Introduction
However, these conventional spatial filtering techniques [17; 18; 19; 20; 21] for image denoising are still faced with the challenge of preservation of image details, which causes the blurring effect, handling of complex noise patterns, parameter tuning, artifacts, and computational complexity, which affect their direct use for medical diagnostic purposes [20; 21]. For instance, [PERSON] [22] proposed a dual-tree DWT combined with wiener filters, used for an image affected by white Gaussian noise, proving that DT-DWT and wiener filters effectively denoise white Gaussian noise. However, the estimation of sub-optimal characteristics led to sub-optimal denoising results. [PERSON] et al. [23] proposed a non-local (NL) means filtering scheme for Gaussian noise removal, where the resemblance of local patches determines the pixel weights. When the window size of the image is reduced to only a one-pixel value, the NL-means filtering becomes the same as the bilateral filter [24]. In [25; 26], median filter and wavelet transform were applied to denoise CT scan images, and better results were achieved. However, there were challenges of blurring and detail preservation, high computational complexity, and edge smudging, which affect the accurate diagnosis and distortion of critical anatomical structures. The DWT effectively reduces noise while preserving image details. However, as the authors of [27] pointed out, selecting an appropriate wavelet basis and thresholding strategy is crucial for balancing noise removal and detail preservation. In [28], mean filters (Mean-F) were used for denoising CT images. However, they blurred the edges and fine details of the image while reducing noise [29]. In [30], Gaussian filters effectively removed random additive noise, such as Gaussian noise, which follows a uniform Gaussian distribution [31]. They did not perform well for large CT scan images, which suffer from unwanted smoothing artifacts that lead to loss of fine details in the image, high computation costs, and blurring of the edges. It is demonstrated that the significant challenges faced by filters in [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31] were edge preservation, image restoration, computational intensity, and the blurring effect problem, which decreases image sharpness, obstructs the view of the underlying anatomy, and renders the CT scan images unsuitable. In recent years, several convolutional neural network (CNN)-based methods have been proposed for natural image denoising, and the application of a three-layer CNN for low-dose CT has shown promising results. Using a deep-CNN improves the image processing performance because of its strong symbolic power. However, when trained with a widely used pixel-level loss function, the CNN-based models often suffer from vanishing gradients by introducing blurring in denoising images [32]. Additionally, striking a balance between noise reduction and retention of clinically relevant information remains a challenge. Therefore, a wavelet-based image deblurring and restoration ensemble approach is proposed to enhance image quality, preserve edge information, and improve image restoration while eliminating the entire image noise. The proposed ensemble approach uses the denoising capabilities of an anisotropic Gaussian filter (AGF), wavelet transform, and denoising convolutional neural network (DnCNN). The AGF is used as a preprocessing operation to reduce Gaussian noise in the image by selectively smoothing the image while preserving the edges and fine details, effectively reducing noise levels. When applied with suitable parameters, it helps reduce blurring effects in the image and restore sharpness. The Haar transform wavelet is a preprocessing operation known for preserving edges due to its ability to capture sharp transitions in the image. An inverse Haar transform is performed to reconstruct the enhanced image. The denoising CNN is trained on pairs of degraded images (blurred and noisy) and their corresponding clean, sharp versions.