Single Image Super-Resolution using Back-Propagation Neural Networks

There are several existing mathematical algorithms for colour image upscaling like Nearest Neighbour, Bicubic and Bilinear. This paper firstly investigates the performances of these three and it has been found that Bicubic performs the best in terms of structural similarity and execution time. A Bicubic with backpropagation based ANN method has been proposed to improve the results. Bicubic with ANN shows 6.5% higher SSIM, 6.9% higher PSNR, 8.7% higher SNR and 30.23% lower MSE values than pure Bicubic. The results of Bicubic with ANN are also compared with state of the art super-resolution techniques like SRCNN. Bicubic with ANN produces 1.48% higher SSIM and 3.44% higher PSNR than SRCNN

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

Context-based adaptive image resolution upconversion

2009-2010 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Deep Learning based data-fusion methods for remote sensing applications

In the last years, an increasing number of remote sensing sensors have been launched to orbit around the Earth, with a continuously growing production of massive data, that are useful for a large number of monitoring applications, especially for the monitoring task. Despite modern optical sensors provide rich spectral information about Earth's surface, at very high resolution, they are weather-sensitive. On the other hand, SAR images are always available also in presence of clouds and are almost weather-insensitive, as well as daynight available, but they do not provide a rich spectral information and are severely affected by speckle "noise" that make difficult the information extraction. For the above reasons it is worth and challenging to fuse data provided by different sources and/or acquired at different times, in order to leverage on their diversity and complementarity to retrieve the target information. Motivated by the success of the employment of Deep Learning methods in many image processing tasks, in this thesis it has been faced different typical remote sensing data-fusion problems by means of suitably designed Convolutional Neural Networks

Image Super-Resolution via Wavelet Feature Extraction and Sparse Representation

This paper proposes a novel Super-Resolution (SR) technique based on wavelet feature extraction and sparse representation. First, the Low-Resolution (LR) image is interpolated employing the Lanczos operation. Then, the image is decomposed into sub-bands (LL, LH, HL and HH) via Discrete Wavelet Transform (DWT). Next, the LH, HL and HH sub-bands are interpolated employing the Lanczos interpolator. Principal Component Analysis (PCA) is used to reduce and to obtain the most relevant features information from the set of interpolated sub-bands. Overlapping patches are taken from the features obtained via PCA. For each patch, the sparse representation is computed using the Orthogonal Matching Pursuit (OMP) algorithm and the LR dictionary. Subsequently, this sparse representation is used to reconstruct a High-Resolution (HR) patch employing the HR dictionary and it is added to the LR image. By applying the quality objective criteria PSNR and SSIM, the novel technique has been evaluated demonstrating the superiority of the novel framework against state-of-the-art techniques

Image Restoration Methods for Retinal Images: Denoising and Interpolation

Retinal imaging provides an opportunity to detect pathological and natural age-related physiological changes in the interior of the eye. Diagnosis of retinal abnormality requires an image that is sharp, clear and free of noise and artifacts. However, to prevent tissue damage, retinal imaging instruments use low illumination radiation, hence, the signal-to-noise ratio (SNR) is reduced which means the total noise power is increased. Furthermore, noise is inherent in some imaging techniques. For example, in Optical Coherence Tomography (OCT) speckle noise is produced due to the coherence between the unwanted backscattered light. Improving OCT image quality by reducing speckle noise increases the accuracy of analyses and hence the diagnostic sensitivity. However, the challenge is to preserve image features while reducing speckle noise. There is a clear trade-off between image feature preservation and speckle noise reduction in OCT. Averaging multiple OCT images taken from a unique position provides a high SNR image, but it drastically increases the scanning time. In this thesis, we develop a multi-frame image denoising method for Spectral Domain OCT (SD-OCT) images extracted from a very close locations of a SD-OCT volume. The proposed denoising method was tested using two dictionaries: nonlinear (NL) and KSVD-based adaptive dictionary. The NL dictionary was constructed by adding phases, polynomial, exponential and boxcar functions to the conventional Discrete Cosine Transform (DCT) dictionary. The proposed denoising method denoises nearby frames of SD-OCT volume using a sparse representation method and combines them by selecting median intensity pixels from the denoised nearby frames. The result showed that both dictionaries reduced the speckle noise from the OCT images; however, the adaptive dictionary showed slightly better results at the cost of a higher computational complexity. The NL dictionary was also used for fundus and OCT image reconstruction. The performance of the NL dictionary was always better than that of other analytical-based dictionaries, such as DCT and Haar. The adaptive dictionary involves a lengthy dictionary learning process, and therefore cannot be used in real situations. We dealt this problem by utilizing a low-rank approximation. In this approach SD-OCT frames were divided into a group of noisy matrices that consist of non-local similar patches. A noise-free patch matrix was obtained from a noisy patch matrix utilizing a low-rank approximation. The noise-free patches from nearby frames were averaged to enhance the denoising. The denoised image obtained from the proposed approach was better than those obtained by several state-of-the-art methods. The proposed approach was extended to jointly denoise and interpolate SD-OCT image. The results show that joint denoising and interpolation method outperforms several existing state-of-the-art denoising methods plus bicubic interpolation.4 month