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matlab code for super resolution of an image using discrete wavelet transform

Abstract

Super resolution is very useful and interesting area of research in image processing applications based on wavelet transform. Many algorithms have been developed by researchers based on Projection Onto Convex Set (POCS), Maximum-aposteriori (MAP) and Maximum Likelihood (ML). In this paper, we propose super resolution algorithm based on Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT). Single frame super resolution can be achieved by use of different interpolation method but this scheme generates blur at the edges of images. Hence in this paper we relied on wavelet transform for super resolution algorithm with different orthogonal and bi-orthogonal filters. Quality aspect of image such as MSE, PSNR, SSIM and Correlation Coefficient are calculated with this proposed algorithm.

Iintroduction

Spatial domain based super-resolution reconstruction techniques, which were designed for uncompressed video to produce high-resolution image or image sequences, may not work well when applied directly to compressed videos, especially to those with severe quantization errors. Here a reconstruction approach designed for videos or image sequences, which were compressed using DWT-based techniques, is presented. This method utilizes the theory of projection onto convex sets (POCS) with a new projection operator based on Discrete Wavelet Transformation (DWT) for reducing blurring artifacts. It also applies maximum a posteriori (MAP) estimation techniques to remove ringing noises from restored images. Experimental results show that such approach could effectively recover objects ' edges and details that were blurred during compressing process while, expanding image 's size simultaneously.