This article is concerned with an effective PDE-based image zooming method which is an improvement of the so-called curvature interpolation method (CIM) previously developed in [H. Kim, Y. Cha, and S. Kim, IEEE Trans. Image Process., 20 (2011), pp. 1895--1903]. The new algorithm incorporates an iterative application of the CIM in order to be an interpolator and to construct a reliable image surface utilizing a generalized curvature source term estimated from the low resolution image. It also employs an effective 9-point scheme for the curvature evaluation which in turn makes the constructed image surface involve no observable ringing artifacts. It has been experimentally verified that the new method results in a high resolution image of clear and sharp edges and cloudless textures, showing qualities of superresolution. Its convergence is analyzed; various results are given to show effectiveness and reliability of the new method. The proposed method has proved not only superior to state-of-the-art methods in image quality but also sufficiently efficient to be applicable for real-time image zooming of arbitrary magnification factors.

We introduce a novel image zooming algorithm, called the curvature interpolation method (CIM), which is partial-differential-equation (PDE)-based and easy to implement. In order to minimize artifacts arising in image interpolation such as image blur and the checkerboard effect, the CIM first evaluates the curvature of the low-resolution image. After interpolating the curvature to the high-resolution image domain, the CIM constructs the high-resolution image by solving a linearized curvature equation, incorporating the interpolated curvature as an explicit driving force. It has been numerically verified that the new zooming method can produce clear images of sharp edges which are already denoised and superior to those obtained from linear methods and PDE-based methods of no curvature information. Various results are given to prove effectiveness and reliability of the new method.

This paper proposes a new interpolation method, called the error-amended sharp edge (EASE) scheme, which is a modified bilinear method. In order to remove/reduce interpolation artifacts such as image blur and the checkerboard effect (ringing), EASE tries to amend the interpolation error by employing the classical interpolation error theorem in an edge-adaptive fashion. EASE is applied for image zooming by both integer and noninteger magnification factors. The new interpolation scheme has proved to result in high-resolution images having clearer and sharper edges than linear interpolation methods, for all synthetic and natural images we have tested. EASE can be implemented with ease; it turns out to be similarly efficient as cubic interpolation schemes.

This article proposes a new interpolation method, called the error-amended sharp edge (EASE) scheme. EASE is a modified bilinear method which tries to amend the interpolation error in an edge-adaptive way by employing the interpolation error theorem. The resulting scheme has proved to result in zoomed images having sharper edges than bilinear, C1-bicubic, and C2-bicubic interpolation methods. EASE is similarly efficient as cubic interpolation methods and can be implemented with ease.

This paper introduces edge-forming schemes for image zooming of color images by general magnification factors. In order to remove/reduce artifacts arising in image interpolation, such as image blur and the checkerboard effect, an edge-forming method is suggested to be applied as a postprocess of standard interpolation methods. The method is based on nonconvex nonlinear partial differential equations. The equations are carefully discretized, incorporating numerical schemes of anisotropic diffusion, to be able to form reliable edges satisfactorily. The alternating direction implicit (ADI) method is employed for an efficient simulation of the model. It has been numerically verified that the resulting algorithm can form clear edges in 2 to 3 ADI iterations. Various results are given to show th eeffectiveness and reliability of the algorithm.