MAP-Based Single-Frame Super-Resolution Reconstruction for Character Image
Li Zha · 2015
Characters are concerned in many image processing applications. Enhance resolutions of character images frequently lead to higher recognition rate. Considering vertical,horizontal and diagonal texture features of character images,a new image smoothness measurement as w ell as a new method for extracting image texture in different directions is proposed,and a single-frame super-resolution( SR) reconstruction method is implemented under the maximum a posteriori( MAP) framework. Using a self-adaptive flexible template as a convolution kernel,texture information is introduced into the image prior model. Therefore,SR reconstruction is finally converted into an objective function optimization problem. Experimental results show that the proposed algorithm effectively increases the recognition rate for character images and has preferable robustness to noise.