Comparison of super-resolution algorithms applied to retinal images

A critical challenge in biomedical imaging is to optimally balance the trade-off among image resolution, signal-to-noise ratio, and acquisition time. Acquiring a high-resolution image is possible; however, it is either expensive or time consuming or both. Resolution is also limited by the physical properties of the imaging device, such as the nature and size of the input source radiation and the optics of the device. Super-resolution (SR), which is an off-line approach for improving the resolution of an image, is free of these trade-offs. Several methodologies, such as interpolation, frequency domain, regularization, and learning-based approaches, have been developed over the past several years for SR of natural images. We review some of these methods and demonstrate the positive impact expected from SR of retinal images and investigate the performance of various SR techniques. We use a fundus image as an example for simulations

Evaluation of neural network based image super-resolution

Abstract. Super-resolution (SR) aims to produce a higher resolution image containing more details than the original image. The amount of pixels is easy to add with simple interpolation methods, but the amount of details does not increase. To overcome this limitation single image super-resolution (SISR) was introduced, which aims to recover the high-resolution (HR) image from the low-resolution (LR) images. Convolutional neural networks (CNN) have become an essential method in machine learning. With the growth of CNN, super-resolution solutions have grown immensely. In this work, a broad review is done on neural network methods designed for super-resolution. Four methods are chosen by their originality and different architectural choices, implemented in PyTorch framework. The models are already trained with public datasets, and the pre-trained models are used for the evaluation. The evaluation is done by analyzing the results with qualitative and quantitative methods. All the methods are tested with public datasets and a private dataset called Hiottu-1, including a wood surface images with different defect types. The evaluation is done based on their image quality and inference time. Peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) metrics are used for quality evaluation, and the inference time is measured by how fast the model generates the output result of test image. The chosen methods improved the image qualities of test images in each datasets. The best perfoming ones were swin image restoration (SwinIR) and pixel attention network (PAN) methods. SwinIR had better PSNR and SSIM values than PAN method and results were pealing to human eye. The inference time of SwinIR is slow, therefore the best possible application would be offline usage. The PAN method had impressing results and its inference time enables the real-time application usage. The SwinIR performed extremely well on Hiottu-1 dataset, with increasing the image quality of defect types and reducing noise overall. The PAN method got high metrics values on Hiottu-1 dataset, although the results were not as pealing as the SwinIR. In the wood manufacturing inspection side, the SwinIR could be utilized on slow production line with high defect detection accuracy, while the PAN method could be utilized on faster production line

An optimal factor analysis approach to improve the wavelet-based image resolution enhancement techniques

The existing wavelet-based image resolution enhancement techniques have many assumptions, such as limitation of the way to generate low-resolution images and the selection of wavelet functions, which limits their applications in different fields. This paper initially identifies the factors that effectively affect the performance of these techniques and quantitatively evaluates the impact of the existing assumptions. An approach called Optimal Factor Analysis employing the genetic algorithm is then introduced to increase the applicability and fidelity of the existing methods. Moreover, a new Figure of Merit is proposed to assist the selection of parameters and better measure the overall performance. The experimental results show that the proposed approach improves the performance of the selected image resolution enhancement methods and has potential to be extended to other methods