9 research outputs found
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
Wavelet-based image and video super-resolution reconstruction.
Super-resolution reconstruction process offers the solution to overcome the high-cost and inherent resolution limitations of current imaging systems. The wavelet transform is a powerful tool for super-resolution reconstruction. This research provides a detailed study of the wavelet-based super-resolution reconstruction process, and wavelet-based resolution enhancement process (with which it is closely associated). It was addressed to handle an explicit need for a robust wavelet-based method that guarantees efficient utilisation of the SR reconstruction problem in the wavelet-domain, which will lead to a consistent solution of this problem and improved performance.
This research proposes a novel performance assessment approach to improve the performance of the existing wavelet-based image resolution enhancement techniques. The novel approach is based on identifying the factors that effectively influence on the performance of these techniques, and designing a novel optimal factor analysis (OFA) algorithm. A new wavelet-based image resolution enhancement method, based on discrete wavelet transform and new-edge directed interpolation (DWT-NEDI), and an adaptive thresholding process, has been developed. The DWT-NEDI algorithm aims to correct the geometric errors and remove the noise for degraded satellite images. A robust wavelet-based video super-resolution technique, based on global motion is developed by combining the DWT-NEDI method, with super-resolution reconstruction methods, in order to increase the spatial-resolution and remove the noise and aliasing artefacts. A new video super-resolution framework is designed using an adaptive local motion decomposition and wavelet transform reconstruction (ALMD-WTR). This is to address the challenge of the super-resolution problem for the real-world video sequences containing complex local motions.
The results show that OFA approach improves the performance of the selected wavelet-based methods. The DWT-NEDI algorithm outperforms the state-of-the art wavelet-based algorithms. The global motion-based algorithm has the best performance over the super-resolution techniques, namely Keren and structure-adaptive normalised convolution methods. ALMD-WTR framework surpass the state-of-the-art wavelet-based algorithm, namely local motion-based video super-resolution.PhD in Manufacturin
Engineering Education and Research Using MATLAB
MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks