Satellite Image Resolution Enhancement Using Dual-Tree Complex Wavelet Transform and Nonlocal Means

To increase the resolution of any image, interpolation techniques are adopted. The high frequency components in the low resolution (LR) image are lost when the images are interpolated. To overcome this problem a new satellite image resolution enhancement algorithm based on Dual Tree Complex Wavelet transform (DTCWT) and its rotated version have been proposed. DTCWT and Rotated DTCWT give 32 subbands of an image, out of which 24 are high frequency (HF) subbands which give 12 different angular information and 8 are low frequency (LF) subbands. The HF subbands are interpolated by Lanczos Interpolation to preserve the high frequency contents of the image. Non Local Means (NLM) filtering is used to eliminate the artifacts which are generated by DTCWT and rotated DTCWT. To obtain the two enhanced high resolution images inverse transforms are performed over respective subbands. The final two high resolution (HR) images are fused together with DTCWT based fusion to give resolution enhanced HR image. To evaluate the performance of the proposed algorithm three performance parameters namely PSNR, SSIM and Q-Index are evaluated for a database of 60 grayscale images of resolution 256×256. The subjective and objective results are compared with the existing techniques to prove the superiority of the proposed algorithm.

Recent deep learning approaches in single image super-resolution (SISR) can generate high-definition textures for super-resolved (SR) images. However, they tend to hallucinate fake textures and even produce artifacts. An alternative to SISR, reference-based SR (RefSR) approaches use high-resolution (HR) reference (Ref) images to provide HR details that are missing in the low-resolution (LR) input image. We propose a novel framework that leverages existing SISR approaches and enhances them with RefSR. Specifically, we refine the output of SISR methods using neural texture transfer, where HR features are queried from the Ref images. The query is conducted by computing the similarity of textural and semantic features between the input image and the Ref images. The most similar HR features, patch-wise, to the LR image is used to augment the SR image through an augmentation network. In the case of dissimilar Ref images from the LR input image, we prevent performance degradation by including the similarity scores in the input features of the network. Furthermore, we use random texture patches during the training to condition our augmentation network to not always trust the queried texture features. Different from past RefSR approaches, our method can use arbitrary Ref images and its lower-bound performance is based on the SR image. We showcase that our method drastically improves the performance of the base SISR approach.

This paper presents a novel single-image super-resolution (SR) procedure, which upscales a given low-resolution (LR) input image to a high-resolution image while preserving the textural and structural information. First, we construct a new type of bivariate rational fractal interpolation model and investigate its analytical properties. This model has different forms of expression with various values of the scaling factors and shape parameters; thus, it can be employed to better describe image features than current interpolation schemes. Furthermore, this model combines the advantages of rational interpolation and fractal interpolation, and its effectiveness is validated through theoretical analysis. Second, we develop a single-image SR algorithm based on the proposed model. The LR input image is divided into texture and non-texture regions, and then, the image is interpolated according to the characteristics of the local structure. Specifically, in the texture region, the scaling factor calculation is the critical step. We present a method to accurately calculate scaling factors based on local fractal analysis. Extensive experiments and comparisons with the other state-of-the-art methods show that our algorithm achieves competitive performance, with finer details and sharper edges.

This paper presents a technique that used to enhance the image resolution. The input low resolution (LR) image is decomposed into low frequency (LF) and high frequency (HF) sub-bands by using lifting wavelet transform (LWT) and discrete wavelet transform (DWT). The resolution enhancement is done by interpolating HF sub-bands that are generated using LWT from the LR image. The HF sub-bands of DWT are used to preserve the edges of the images. The operation is done on HF sub-bands of LWT and DWT. The HF sub-bands that are generated from the DWT are interpolated and perform addition to the respective interpolated HF sub-bands of LWT. The HF sub-bands that are modified and the LR image will again interpolate and undergoes the inverse lifting wavelet transform (ILWT) to reconstruct the high resolution (HR) image. This implemented method results efficient over conventional methods.

Watermarking is one of the widely used applications to provide security for the content shared over internet. Internet is a place where the data is not of assured security to the fullest. DTCWT is widely used in all image processing applications over a decade. A novel imperceptible video watermarking scheme is proposed. This method is implemented with the use of Dual Tree Complex Wavelet Transform (DTCWT) and Singular value decomposition (SVD) which helps in proof of ownership. In this method Singular Value Decomposition is applied to the Dual Tree Complex Wavelet Transform (DTCWT) coefficients of both watermark and original image and the singular Eigen values are interchanged. Because of advantages, Shift invariance and Directionality we prefer DTCWT. As SVD decomposes the matrix into 3 matrices U, S, V, we need U, V at the time of extraction. So we use U, V as watermark in audio. Watermark extracted from the audio gives the U, V matrices which makes this method blind watermarking. In order to prove the robustness of the method the results are compared with similar algorithms proposed in this paper but with DWT.

Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between Peak-Signal-to-Noise Ratio (PSNR) performances and computational complexity. However, since SI only utilizes simple linear mappings, it may fail to precisely reconstruct HR patches with complex texture. In this paper, we present a novel SR method, which inherits the large-to-small patch conversion scheme from SI but uses global regression based on local linear mappings (GLM). Thus, our new SR method is called GLM-SI. In GLM-SI, each LR input patch is divided into 25 overlapped subpatches. Next, based on the local properties of these subpatches, 25 different local linear mappings are applied to the current LR input patch to generate 25 HR patch candidates, which are then regressed into one final HR patch using a global regressor. The local linear mappings are learned cluster-wise in our off-line training phase. The main contribution of this paper is as follows: Previously, linear-mapping-based conventional SR methods, including SI only used one simple yet coarse linear mapping to each patch to reconstruct its HR version. On the contrary, for each LR input patch, our GLM-SI is the first to apply a combination of multiple local linear mappings, where each local linear mapping is found according to local properties of the current LR patch. Therefore, it can better approximate nonlinear LR-to-HR mappings for HR patches with complex texture. Experiment results show that the proposed GLM-SI method outperforms most of the state-of-the-art methods, and shows comparable PSNR performance with much lower computational complexity when compared with a super-resolution method based on convolutional neural nets (SRCNN15). Compared with the previous SI method that is limited with a scale factor of 2, GLM-SI shows superior performance with average 0.79 dB higher in PSNR, and can be used for scale factors of 3 or higher.

We present a self-learning approach for single image super-resolution (SR), with the ability to preserve high frequency components such as edges in resulting high resolution (HR) images. Given a low-resolution (LR) input image, we construct its image pyramid and produce a super pixel dataset. By extracting context information from the super-pixels, we propose to deploy context-specific contour let transform on them in order to model the relationship (via support vector regression) between the input patches and their associated directional high-frequency responses. These learned models are applied to predict the SR output with satisfactory quality. Unlike prior learning-based SR methods, our approach advances a self-learning technique and does not require the self similarity of image patches within or across image scales. More importantly, we do not need to collect training LR/HR image data in advance and only require a single LR input image. Empirical results verify the effectiveness of our approach, which quantitatively and qualitatively outperforms existing interpolation or learning-based SR methods.

Image super-resolution (SR) produces a high-resolution (HR) image using either a single or multiple low-resolution (LR) input image(s) of the same scene. Sparse representation techniques are effectively applied for image SR because of their high reconstruction accuracy. SR reconstruction is accomplished through learning of an overcomplete dictionary and then solving a series of regularization problems applied on each patch extracted from the input image. This paper demonstrates a sparse representation based coupled overcomplete dictionary training and SR procedure for LR multispectral images. The proposed work is also implemented using a multicore parallel processing technique to provide faster reconstruction. Experimental results show superiority of the proposed method over some others.

The improvement of the spatial resolution of natural images is an important task for many practical applications of current image processing. The endeavor of applying single image super-resolution, which only uses one low-resolution input image, is frequently hampered by the presence of noise or artifacts in the input image, so denoising and super-resolution algorithms are usually applied to obtain a noiseless high-resolution image. In this work, several traditional and deep learning methods for denoising and super-resolution are hybridized to ascertain which combinations of them yield the best possible quality. The experimental design includes the introduction of Gaussian noise, Poisson noise, salt-and-pepper noise, and uniform noise into the low-resolution inputs. It is found that denoising must be carried out before super-resolution for the best results. Moreover, the obtained results indicate that deep learning techniques clearly outperform the traditional ones.

Face image super-resolution (FSR) algorithms are capable of providing a high-resolution image from an input low-resolution (LR) image. Various FSR algorithms use a set of training examples to reconstruct the input LR image. For the purpose, proper weights need to be calculated for each training image. In general, the least square estimation approach is used for obtaining optimal reconstruction weights, known as least square representation (LSR) problem. In this paper, to minimize LSR problem more effectively, a grey wolf optimizer (GWO) based FSR algorithm (FSR-GWO) is proposed. To make search process of GWO algorithm suitable to FSR, a new formulation for upper-bound and lower-bound is introduced. Performance comparison with state-of-the-art nature-inspired algorithms and several super-resolution methods on FEI public face database shows the effectiveness of the proposed FSR-GWO algorithm.

Positron Emission Tomography (PET) is a nuclear, in-vivo medical imaging technology which can make 3D images of tissue metabolic act, in which high dose of tracer is needed to obtain a high quality PET image, which affects patients' health. Due to the limitations of using high dose tracer and limitations of physical imaging systems, it is not easy to get an image in desired resolution. Simplest approach to generate a high resolution image is by post processing. Single Image Super Resolution (SISR) is a post processing procedure to retrieve a high resolution image from a low resolution input. We propose a convolutional neural network trained on PET images which can estimate a high resolution PET image from its input low resolution image.

This paper presents the original and versatile architecture of a modular neural network and its application to super-resolution. Each module is a small multilayer perceptron, trained with the Levenberg-Marquardt method, and is used as a generic building block. By connecting the modules together to establish a composition of their individual mappings, we elaborate a lattice of modules that implements full connectivity between the pixels of the low-resolution input image and those of the higher-resolution output image. After the network is trained with patterns made up of low and high-resolution images of objects or scenes of the same kind, it will be able to enhance dramatically the resolution of a similar object's representation. The modular nature of the architecture allows the training phase to be readily parallelized on a network of PCs. Finally, it is shown that the network performs global-scale reconstruction of human faces from very low resolution input images.

Most of the previous models performed well for Single Image Super-Resolution (SISR). In these methods, the Low Resolution (LR) input image is amplified to the size of High Resolution (HR) through bicubic interpolation. However, bicubic interpolation can not represent the high frequency features of images with only one filter. Therefore, in this paper, we used a original framework which can effectively extract the feature maps from the input image space and transform to HR feature maps based on Spatial Transformer Networks (STN). In our STN-SR method, there are three kinds of parameters should be learned from the model: (i) a serial of filters to extract LR image feature maps; (ii)a local small network to learn parameters of the transformation $\Gamma_{\theta}(G)$ and (iii) the filter parameters to restore the HR patchs from the input HR feature maps through a restoring layer. Our model directly focus on the whole image, the proposed STN-SR method does not clip the image into many small size patches, and can use the image gobal message to rebuild more robust local texture. Compared to privious SR methods, the proposed STN-SR method can gain completely real image, while illustrating better edge and texture preservation performance.

Proposed weighted transformation and wavelet transforms-based image resolution and contrast enhancement can be described in two phases. In first phase, input low-resolution image is resolution-enhanced using lifting–stationary wavelet transforms. In this process, input image is decomposed into low-frequency and high-frequency components using lifting and stationary wavelet transforms, and high-frequency components are bi-cubic interpolated to improve the resolution. Finally, inverse lifting wavelet transform is applied to get the high-resolution image. In second phase, resolution-enhanced image is applied to contrast enhancement using weighted transformation function which is obtained from adaptive histograms and mean values from similarities and dissimilarities. Proposed method shows the superiority in experimental results in both quantitatively and visually. Proposed methods are compared with the existing and method with mean square error, Peak signal to noise ratio, noise estimation and entropy and also with visual results.

A new multi-frame image super resolution (SR) algorithm via Bayesian modeling with natural image prior modeled by fields of experts (FoE) is proposed. Multi-frame SR can be used to obtain a high resolution (HR) image from a set of degraded low resolution (LR) images without changing any hardware device. However, SR is well known to be an ill-posed problem. So state-of-the-art solutions usually formulate the problem with Bayesian modeling techniques, which infer the HR image based on not only the LR input images but also on prior information about the HR image. Current Bayesian SR approaches typically use simple prior models such as L1 norm, TV prior and Laplacian prior, which cannot exploit the statistics of natural scenes well. In this paper, a Bayesian multi-frame image SR approach using a FOE model as the prior for natural images is presented. The Maximum a Posteriori (MAP) framework is used for estimating the HR image. The proposed method cannot only capture the statistics of natural images well, but also require less computational power than the other Bayesian modelling methods such as Sampling methods and Approximate inference. The proposed method shows superior or comparable results to the state-of-art multi-frame SR methods.