Fast single frame super-resolution using scale-invariant self-similarity

Example-based super-resolution (SR) attracts great interest due to its wide range of applications. However, these algorithms usually involve patch search in a large database or the input image, which is computationally intensive. In this paper, we propose a scale-invariant self-similarity (SiSS) based super-resolution method. Instead of searching patches, we select the patch according to the SiSS measurement, so that the computational complexity is significantly reduced. Multi-shaped and multi-sized patches are used to collect sufficient patches for high-resolution (HR) image reconstruction and a hybrid weighting method is used to suppress the artifacts. Experimental results show that the proposed algorithm is 201,800 times faster than several state-of-the-art approaches and can achieve comparable quality.published_or_final_versio

Image enlargement using multiple sensors

Image sensing is generally performed with multiple spectral sensors. For example, combination of three sensors (red, green, and blue) is used for color image reproduction, and electrooptical and infrared sensors are used for surveillance and satellite imaging, respectively. The resolution of each sensor can be intensified by taking the other sensors into account and applying correlations between different sensors. There are various successful applications of image enlargement using multiple sensors and even multimodal sensors. However, there still are several open issues in sensor processing which can be explained by signal processing-based image enlargement using redundancy among the sensors

Face image super-resolution using 2D CCA

In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches. © 2013 Elsevier B.V

Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement

In this paper, we propose a novel compressed image super resolution (CISR) framework based on parallel and series integration of artifact removal and resolution enhancement. Based on maximum a posterior inference for estimating a clean low-resolution (LR) input image and a clean high resolution (HR) output image from down-sampled and compressed observations, we have designed a CISR architecture consisting of two deep neural network modules: the artifact reduction module (ARM) and resolution enhancement module (REM). ARM and REM work in parallel with both taking the compressed LR image as their inputs, while they also work in series with REM taking the output of ARM as one of its inputs and ARM taking the output of REM as its other input. A unique property of our CSIR system is that a single trained model is able to super-resolve LR images compressed by different methods to various qualities. This is achieved by exploiting deep neural net-works capacity for handling image degradations, and the parallel and series connections between ARM and REM to reduce the dependency on specific degradations. ARM and REM are trained simultaneously by the deep unfolding technique. Experiments are conducted on a mixture of JPEG and WebP compressed images without a priori knowledge of the compression type and com-pression factor. Visual and quantitative comparisons demonstrate the superiority of our method over state-of-the-art super resolu-tion methods.Code link: https://github.com/luohongming/CISR_PS

FULLY NON-LOCAL SUPER-RESOLUTION VIA SPECTRAL HASHING

Super-resolution is the task of creating an high resolution image from a low resolution input sequence. To overcome the difficul- ties of fine image registration, several methods have been proposed exploiting the non-local intuition, i.e. any datapoint can contribute to the final result if it is relevant. These algorithms however limit in practice the search region for relevant points in order to lower the corresponding computational cost. Furthermore, they define the non-local relations in the high resolution space, where the true im- ages are unknown. In this work, we introduce the use of spectral hashing to effi- ciently compute fully non-local neighbors. We also restate the super- resolution functional using fixed weights in the low resolution space, allowing us to use resolution schemes that avoid many artifacts