Quality Of Low-resolution Images Research Articles

Super-Resolution of Images using Contrastive Self-Learning

Abstract: Super-resolution (SR) techniques aim to enhance the resolution and quality of low-resolution images. In recent years, deep learning-based approaches have achieved remarkable success in this field. However, training deep SR models often requires large-scale datasets and computationally expensive operations, limiting their practicality. Different approaches have been proposed to tackle computation and time constraints. Our novel approach involves a combination of knowledge distillation and contrastive loss function to train a compact and efficient contrastive self-distillation (CSD) model. In the framework, a teacher network is initially trained on a large dataset using a traditional supervised learning approach. The teacher network learns to generate high-resolution images from low-resolution inputs. Subsequently, a student network is initialized with the same architecture as the teacher network but with fewer parameters. The student network is then trained to mimic the behavior of the teacher network by utilizing a contrastive loss. The contrastive loss is formulated by constructing positive and negative pairs of low-resolution and high-resolution image patches. The proposed CSD method achieves competitive performance compared to state-of-the-art SR methods while requiring significantly fewer parameters and computational resources. Furthermore, it demonstrates improved generalization capability by effectively reconstructing details in real-world images.

Suitability of Single Image Super Resolution Models for Video Super Resolution

This project is an attempt to understand the suitability of the Single image super resolution models to video super resolution. Super Resolution refers to the process of enhancing the quality of low resolution images and video. Single image super resolution algorithms refer to those algorithms that can be applied on a single image to enhance its resolution. Whereas, video super resolution algorithms are those algorithms that are applied to a sequence of frames/images that constitute a video to enhance its resolution. In this paper we determine whether single image super resolution models can be applied to videos as well. When images are simply resized in Open CV, the traditional methods such as Interpolation are used which approximate the values of new pixels based on nearby pixel values which leave much to be desired in terms of visual quality, as the details (e.g. sharp edges) are often not preserved. We use deep learning techniques such as GANs (Generative Adversarial Networks) to train a model to output high resolution images from low resolution images. In this paper we analyse suitability of SRGAN and EDSR network architectures which are widely used and are popular for single image super resolution problem. We quantify the performance of these models, provide a method to evaluate and compare the models. We further draw a conclusion on the suitability and extent to which these models may be used for video super resolution. If found suitable this can have huge impact including but not limited to video compression, embedded models in end devices to enhance video output quality.

Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity

BackgroundLow-resolution images may be acquired in magnetic resonance imaging (MRI) due to limited data acquisition time or other physical constraints, and their resolutions can be improved with super-resolution methods. Since MRI can offer images of an object with different contrasts, e.g., T1-weighted or T2-weighted, the shared information between inter-contrast images can be used to benefit super-resolution.MethodsIn this study, an MRI image super-resolution approach to enhance in-plane resolution is proposed by exploring the statistical information estimated from another contrast MRI image that shares similar anatomical structures. We assume some edge structures are shown both in T1-weighted and T2-weighted MRI brain images acquired of the same subject, and the proposed approach aims to recover such kind of structures to generate a high-resolution image from its low-resolution counterpart.ResultsThe statistical information produces a local weight of image that are found to be nearly invariant to the image contrast and thus this weight can be used to transfer the shared information from one contrast to another. We analyze this property with comprehensive mathematics as well as numerical experiments.ConclusionExperimental results demonstrate that the image quality of low-resolution images can be remarkably improved with the proposed method if this weight is borrowed from a high resolution image with another contrast.Graphical Multi-contrast MRI Image Super-resolution with Contrast-invariant Regression Weights