A bicubic interpolation approach is generally executed in the same manner of the bilinear interpolation approach. It takes into consideration the nearest 4×4 neighborhood of known pixels. Since, they are at different distances from the unidentified pixel. The bicubic interpolation approach can relatively obtain a clear image quality. However, it requires a greater amount of computation. Therefore, this approach generates visibly sharper images in comparison to the previous two approaches. It perhaps gets the optimal mixture of processing time and output quality (Xin Li & Orchard, 2001). Additionally, this approach can be widely used in a large number of image processing applications such as Adobe Photoshop, Adobe After Effects, Avid and Macromedia Final Cut Pro etc. A New Edge Directed Interpolation (NEDI) (Xin Li & Orchard, 2001) is another approach. The interpolated pixels are approximated from the local covariance parameters of the LR images depending on the geometric duality among the LR and HR covariance.

An Edge Guided Interpolation (EGI) approach (L. Zhang & Wu, 2006) splits the neighbor of every pixel to make a couple observation subsets through the orthogonal directions and estimate the lacking pixel. This approach merges both of the estimated values into the powerful estimation by applying linear-minimum mean square error estimation. A gradient-based adaptive interpolation (Chu, Liu, Qiao, Wang, & Li, 2008) takes into consideration the distance among the interpolated pixel and the nearby respected pixel. The results of this suggested technique increases and enhances the quality of recovered images. Furthermore, it is a powerful technique to detect the registration mistake and needs a low-computational cost.A cubic spline approach (X. Zhang & Liu, 2010) meets a piecewise continuing curve and moving through lots of points. The fundamental job of the cubic spline interpolation approach is to compute weights that are used to interpolate the information. The registration, interpolation, and restoration steps in the SR approach can be executed to accomplish the HR image that comes from a series of LR images through an Iterative Back Projection (IBP) approach (Irani & Peleg, 1991). In IBP approach, the HR image is approximated by reducing the error among the simulated and observed LR images. This approach is extremely easy to understand and very simple. However, it is not generally going to give an unique result because of the ill-posed trouble. An additional simply implemented SR approach is the Projection Onto Convex Set (POCS) approach that has been developed by Stark and Oskoui (Stark & Oskoui, 1989). In POCS approach, a set of restrictions are described to limit the space of HR image. The restriction sets are curved and facilitated the particular attractive of SR image features such as positivity, smoothness, bounded energy, and dependability. The intersection coming from all these sets represents the area of the allowable solution. As a result, this problem is minimized to locating the intersection of the restriction sets. The projecting operators are decided for every convex restriction set to get the solution. This operator reflects the primary estimation of the HR image against the relevant restriction set. Repetitively executing this method, a great solution is acquired at the area of intersection of the k convex restriction sets. This approach actually does not integrates any observation noise.

In order to enhance the quality of an image, various methods are suggested to improve the interpolation based approaches such as:

Ur and Gross (Ur & Gross, 1992) execute a non-uniform interpolation of a couple of spatially shifted LR images. They use the generalized multi-channel sampling theorem. The benefit of this method is the low-computational cost, which it is actually ideal for real-time applications. However, the ideality of the whole rebuilding process is not assured, because of the interpolation mistakes are not considered. Komatsu et al. (Komatsu, Aizawa, Igarashi, & Saito, 1993) show a new scheme to obtain a better resolution image. They apply the Landweber algorithm at multiple images concurrently with multiple cameras. In addition, they make use of the block-matching approach to measure comparative shifts. If the cameras currently have the same aperture, it enforces serious restrictions both in their agreement and in the configuration of the scene. Bose and Ahuja (N. K. Bose & Ahuja, 2006) make use of the moving least square (MLS) approach to approximate the intensity value at each pixel position of the HR image.