Saliency Guided Image Super-Resolution using PSO and MLP based Interpolation in Wavelet Domain

Super resolution algorithms always used as a tradeoff between the cost of the high definition (HD) cameras and the quality and/or clarity of the image obtained. There are various predefined algorithms that obtain Super Resolved images from Low Resolution (LR) images, some (such as, Convolutional Neural Network (CNN), Deep learning, Sparse Representation based algorithms) gives better results for e.g. deburring of zoomed part, removal of noise, color enhancement and so on but are computationally complex or hard to implement in real-time environment whereas some are very simple to use (such as, interpolation based, wavelet based algorithms) but lack quality for e.g. ringing artifacts, edge blurs, poor image quality etc. In this paper, we proposed a method that combines advantages of some of the above mentioned methods. Our proposed method obtains High Resolution (HR) image using saliency model for detection of visually dominant regions, Discrete Wavelet Transform (DWT) for extraction of high frequency details, finally Multi-layer Perceptron (MLP) and Particle Swarm Optimization (PSO) for interpolation. Experimental results visually and quantitatively show that for considered test images our proposed super resolution method appears to be most promising compared to bi-cubic, Chopade et al., Yu et al. and Man et al. methods.