Single Image Super Resolution:Perceptual quality & Test-time Optimization
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Image super resolution is defined as recovering a high-resolution image given a low-resolution image input. It has a wide area of applications in modern digital image processing, producing better results in areas including satellite image processing, medical image processing, microscopy image processing, astrological studies and surveillance area. However, image super resolution is an ill-posed question since there exists non-deterministic answer in the high resolution image space, making it difficult to find the optimal solution.
In this work, various research directions in the area of single image super resolution are thoroughly studied. Each of the proposed methods' achievements as well as limitations including computational efficiency, perceptual performance limits are compared. The main contribution in this work including implementing a perceptual score predictor and integrating as part of the objective function in the upsampler algorithm. Apart from that, a test-time optimization algorithm is proposed, aiming at further enhance the image quality for the obtained super-resolution image from any upsampler. The proposed methods are implemented and tested using Pytorch. Results are compared on baseline applied datasets including Set5, Set14, Urban100 and DIV2K.
Results from perceptual score predictor was evaluated on both PSNR precision index and perceptual index, which is a combination of perceptual evaluation Ma score and NIQE score. With new objective function, the upsampler achieved to move along the trade-off curve of precision and perception. The test-time optimization algorithm achieved slightly improvements in both precision and perception index. Note that the proposed test time optimization does not require training of new neural network, thus, is computationally efficient.
Deep Neural Networks
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Rights for Collection: Masters Theses