## 1 Introduction  
Images are being used across various domains of today's digital world. Examples of those areas include photography, digital entertainment, computer vision, remote sensing, medical diagnostics, microscopy, space science, and surveillance. Unfortunately, images tend to suffer from degradation during their formation and transmission processes. Such degradations come in many forms, such as noise, blur, intensity non-uniformity, missing pixels because of electronic or sensor failures, and interference due to neighboring electronic devices. These degradations will not only influence the visual quality of images but will also reduce their interpretability, and the effectiveness of image analysis and image processing algorithms. As part of this, restoring and enhancing degraded images is a very important and demanding area that aims to reduce the influence of various degradations and enhance the quality and interpretability of such images. The purpose of enhancement and restoration techniques is to increase the visual appearance and perceptual quality of images, thus increasing their effectiveness for various desired applications such as image recognition [1], visual object detection [2], and semantic segmentation [3]. Enhancement techniques are assigned to improve particular visual qualities such as contrast, sharpness, or color balance. Image restoration techniques, on the other hand, focus on particular kinds of degradation in the image. Image restoration techniques include denoising to remove noise, deblurring processes to recover sharpness, and inpainting to recover missing regions. Noisy images are very important to remove for interpretation and processing purposes. These types of images are inefficient for processing. Some fundamental unsupervised or supervised algorithms include face print-based identification, hidden identification [4], object detection purposes [5], and image segmentation. Over time, significant developments have been made in image restoration and enhancement, powered by advancements in computer vision, machine learning, and signal processing [6]. Traditional approaches depend on algorithms that influence mathematical models and traditional rules. However, these methods often struggle with complex degradation patterns and require robust generalization across diverse image types. In recent years, deep learning has changed the way images are processed and restored. Platforms like CNNs and all types of models that are part of deep learning, either encoder-decoder networks [7; 8; 9; 10] or high-resolution models [11; 12; 13; 14], are surprisingly good for image processing. A large amount of data in deep learning models and large-scale training makes it possible to learn complex mappings from the input (degraded image) to the output (pristine image) and to learn the task much better than traditional models, leading to more accurate and subjectively realistic image restoration and enhancement. Our method provides a novel approach to image restoration and enhancement. The proposed approach addresses two specific problems in this area: denoising and super-resolution. Our main goal is to deeply explore the up-to-date and futuristic methods for image restoration and enhancement across the two specific challenges we have chosen. Through this process, our goal was to find unique supervised deep CNN techniques that will help improve image restoration and enhancement based on generated low-resolution images. Ultimately, we try to contribute by providing novel research into the development of image restoration and enhancement techniques while relying on aspects of supervised CNNs. To start our discussions, we take up the topic of denoising a noisy image. Denoising is known to be a classically difficult problem since noise reduction tends to remove the fine details that are important for understanding; that is why classic methods of denoising either tend to provide a lot of noise along with details or tend to eradicate details along with noise.