1 Abstract: Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may exhibit insufficient spatial and temporal resolution. Super-Resolution (SR) image reconstruction is a promising technique of digital imaging which attempts to reconstruct High Resolution (HR) imagery by fusing the partial information contained within a number of under-sampled low-resolution (LR) images of that scene during the image reconstruction process. Super- resolution image reconstruction involves up-sampling of under- sampled images thereby filtering out distortions such as noise and blur. In comparison to various image enhancement techniques, super-resolution image reconstruction technique not only improves the quality of under-sampled, low-resolution images by increasing their spatial resolution but also attempts to filter out distortions. Not only do such HR images give the viewer a more pleasing picture but also offer additional details that are significant for subsequent analysis in many applications. Super Resolution Reconstruction (SRR) algorithm is considered to be one of the most promising techniques that can help to overcome the limitations due to optics and sensor resolution. Keywords: Super resolution, digital image reconstruction, higher-resolution images, algorithmic advances. 1. INTRODUCTION Super-resolution is the task of obtaining a high-resolution image of a scene given low resolution image(s) of the scene. Applications of super-resolution include satellite, forensic, medical imaging, surveillance et al. [1]. Obtaining high- resolution images directly via better hardware (better image sensors, larger chip size) is quite costly. Image interpolation methods are not considered as super-resolution methods since even the ideal sinc interpolation cannot recover the high frequency components that are lost in the low- resolution sampling process. Most of the super-resolution approaches work on the principle of combining multiple slightly-shifted low-resolution images of the scene [2]. This involves image registration, interpolation and deblurring as the basic operations. However, this technique is numerically limited to small increases in resolution. There also exist methods based on techniques like gradient profile priors [3] and sparse representation of images in an over-complete dictionary [4]. A recent approach based on a single image was proposed in [5] that exploits the redundancy of patches within the image and combines this information with example-based techniques such as [6]. There have been several different approaches to super- resolution, with estimation of high-resolution (HR) images from multiple low resolution (LR) observations related by small motions being by far the most common one. Most of these methods are based on accurate registration and solve the super resolution reconstruction using variants of gradient descent with or without a smoothness prior [7-10]. Super- resolution has also been tried from multiple defocused images [11], varying zoom [12] and photometric cues [13]. Reconstruction based approaches to super-resolution model the low resolution image formation process to establish a relation between the unknown high resolution image and the low resolution observations, and use the relationship to derive algorithms to estimate the high resolution image essentially by an inversion process [11-15]. The inversion process is typically ill-conditioned and it often necessitates the use of smoothness or other priors [14, 16, 17] to obtain reasonable solutions. In [18], Baker and Kanade examine the limits of such processes and derive that for most point spread functions and blur kernels the estimation process is non-invertible or ill-conditioned. Further, the number of possible solutions grow at least quadratically with the desired magnification factor. They also show that this large growth in the number of solutions makes super-resolution difficult even with smoothness priors and the resulting solutions often fail to recover the high frequency details. 2. PRINCIPLE OF SUPER-RESOLUTION IMAGE RECONSTRUCTION 2.1 Definition of Super Resolution The term super-resolution refers to the construction of an image whose resolution is higher than the resolution provided by the sensor used in the imaging system. Optical resolution is a measure of the ability of a camera system, or a component of a camera system, to depict picture detail. On the other hand, image resolution is defined as the fineness of detail that can be clearly distinguished in an image. Both the definitions apply to digital and analogue camera systems and images. However, in this research, the term resolution will only relate to digital camera systems and digital images. There are two most common classifications of digital image resolution, namely – spatial and bit-depth. • Spatial resolution refers to the level of detail discernable in an image. • Bit-depth refers to the number of bits or 0's and 1's that can be used to specify the colour at each pixel of an image. 2.2 Principle Super-resolution image reconstruction is based on the theory of Analytic Continuation, which means reconstruction of the whole analytic function according to its values in certain A Survey on Image Reconstruction Using Super Resolution Anil B. Gavade*, Vijay S. Rajpurohit** *Department of Electronics and Communication Engineering **Department of Computer Science and Engineering KLS Gogte Institute of Technology, Belgaum-59008, INDIA. anil.gavade@gmail.com, vijaysr2k@yahoo.com