## 2 Related Work  
The image restoration problem has been well-examined with the advancement in techniques over the past few years [15; 16; 17; 18]. Several methods were introduced with various threats and trials in different restoration fields [14; 19; 20]. In the modern era, techniques including trainable neural networks replace conventional techniques [21; 22; 23; 24], even skipping pre-assumption degradation procedures. The introduction of transformers provides new ways to approach the restoration domain. Some of them were originally designed for NLP tasks [25]. Vision Transformers break down the image into sequential patches, study their dependencies through relativity and capabilities, and illustrate images by cultivating the input data that entirely depend on self-attention [26]. Later, they are used for denoising, super-resolution, de-raining, and image colorization tasks [27; 28; 29]. The advancement in transformers that lessens the complications while developing sharper visions gives more precise results [30; 31; 32]. In addition, low-rank factorization and approximation approaches [33; 34; 35; 36] ignite transformers yet lead to information loss, parameters, and task dependency. **Denoising**. One crucial problem in image restoration is image denoising, which targets removing undesired noise while retaining important details. The traditional denoising approaches are based on filters like the median filter, the Wiener filter, and the wavelet transform, and on transforming coefficients and masking. Several patch-based methods [37; 38; 39] were also introduced, which resulted in redundancy in visuals. These types of techniques work in either the spatial or frequency domain. They are also built on simple assumptions about the noise (uniformly Gaussian distributed or multiplicative white noise) and/or the image (e.g., Gaussian distribution of DCT coefficients, piecewise smooth behavior) conditions. Deep learning-based methods have made huge progress in image denoising [40; 41; 42; 43; 44; 45]. Convolutional Neural Networks (CNNs) have yielded impressive image denoising by effectively learning noise patterns (filters) and their handling. The latest DCNN (Deep Convolutional Neural Network) obtained the best performance in a wide range of denoising scenarios. **Super-Resolution**. Super-resolution is the process of generating corresponding high-resolution (HR) images or videos from one or more low-resolution (LR) observations. The traditional methods are mainly devoted to statistical models or interpolation. However, to generate HR images with a natural appearance, including sharp edges and fine textures, many of them were based on sampling theory [46], edge-guided interpolation [47], natural image generation [48], and sparse illustration [49]. Recently, the progress of learning techniques for classification and other categories has also been remarkable. There are super-resolution (SR) approaches based on deep learning, such as the super-resolution (SR) reconstruction based on a simple CNN (SRCNN) [11] model and the non-linear mapping of high-resolution (SR) reconstruction based on CNN models (EDSR) [39] that follow the improvement of the model results. Deep learning techniques help to learn more about the inherent characteristics of high-resolution images. Different data-based techniques have different design frameworks [50; 51]. While traditional methods directly produce HR images from LR images [52; 53; 54; 55], modern techniques introduce residual learning architecture [55] for processing high-frequency image information. Apart from that, dense connections [56; 57], multi-branch learning [58; 59], progressive reconstruction [60], generative adversarial networks (GANs) [13; 50; 61; 62], and recursive learning are also some of the methods for performing super-resolution tasks. Fraunhofer Institute in Germany has developed methods based on recursive learning [63], Non-local Means Filtering based on Variational Models for SR, and exponential cross-diffusion.