Unified Dictionary Training Approach for High-Resolution Image Enhancement

This research presents a new unified dictionary training super-resolution (UDTSR) approach to single and multi-image super-resolutions that uses a bilevel optimization frame-work and patchwise sparse recovery. By employing interconnected dictionaries to bridge the two spaces of image patches, we ensure that a sparse representation of a low-resolution (LR) image patch can accurately reconstruct its corresponding high-resolution (HR) patch. To facilitate efficient stochastic gradient descent, implicit differentiation calculates the gradient. Further-more, by using a neural network model for rapid sparse inference and selective processing of visually essential areas, we can almost tenfold improve the performance of real-world applications. Also, by discovering the fundamental relationships between different data modalities, our approach overcomes the difficulty of dealing with panchromatic and multispectral images. For example, using shared and individual sparse representations, we describe a data model that can detect similarities and differences in multimodal signals. Single image super-resolution (SISR) and multi-frame super-resolution (MFSR) are advanced separately, with minimal research on their ideal combination. We propose a novel UDTSR analysis using an iterative shrinkage and thresholding algorithm. Our simulations of many combinations of SISR and MFSR, such as x2, x3, and x4, confirm our theory quantitatively and qualitatively.