Abstract
Different from the existing super-resolution (SR) reconstruction approaches working under either the frequency-domain or the spatial- domain, this paper proposes an improved video SR approach based on both frequency and spatial-domains to improve the spatial resolution and recover the noiseless high-frequency components of the observed noisy low-resolution video sequences with global motion. An iterative planar motion estimation algorithm followed by a structure-adaptive normalised convolution reconstruction method are applied to produce the estimated low-frequency sub-band. The discrete wavelet transform process is employed to decompose the input low-resolution reference frame into four sub-bands, and then the new edge-directed interpolation method is used to interpolate each of the high-frequency sub-bands. The novelty of this algorithm is the introduction and integration of a nonlinear soft thresholding process to filter the estimated high-frequency sub-bands in order to better preserve the edges and remove potential noise. Another novelty of this algorithm is to provide flexibility with various motion levels, noise levels, wavelet functions, and the number of used low-resolution frames. The performance of the proposed method has been tested on three well-known videos. Both visual and quantitative results demonstrate the high performance and improved flexibility of the proposed technique over the conventional interpolation and the state-of-the-art video SR techniques in the wavelet- domain.
Highlights
High resolution (HR) images and videos are highly desirable, and strongly in demand for most electronic imaging applications for providing better visualisation and for extracting additional information
This paper proposes a robust video super-resolution approach, based on a combination of the so-called discrete wavelet transform-new edge-directed interpolation and a soft-thresholding for increasing the spatial resolution and recovering the noiseless high-frequency details of the observed noisy LR video frames with global motion, which integrates merits from the methods of image registration and reconstruction in both frequency-domain and spatial-domain
A robust video super-resolution reconstruction approach based on combining discrete wavelet transform, new edge-directed interpolation and the nonlinear soft-thresholding has been proposed in this paper for noisy LR video sequences with global motion to recover the noiseless high-frequency details and increase the spatial resolution, which integrates properties from methods of image registration and reconstruction
Summary
High resolution (HR) images and videos are highly desirable, and strongly in demand for most electronic imaging applications for providing better visualisation and for extracting additional information. It is essential to find an effective way in image processing to increase the resolution level at a low-cost, without replacing the existing imaging system. To address this challenge, the concept of super-resolution (SR) has been sought after. The concept of super-resolution (SR) has been sought after This technique aims to produce a single HR image, or HR video, from a set of different successive low-resolution (LR) images captured from the same scene in order to overcome the limitations and/or possibly ill-posed conditions of the imaging system [53]. Due to its wide applications, SR has been an active area of research over the last two decades for a variety of applications, such as satellite imaging [10, 21], medical imaging [19, 46], forensic imaging [29, 47] and video surveillance systems [20, 67]
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