A New Method for Superresolution Image Reconstruction Based on Surveying Adjustment

A new method for superresolution image reconstruction based on surveying adjustment method is described in this paper. The main idea of such new method is that a sequence of low-resolution images are taken firstly as observations, and then observation equations are established for the superresolution image reconstruction. The gray function of the object surface can be found by using surveying adjustment method from the observation equations. High-resolution pixel value of the corresponding area can be calculated by using the gray function. The results show that the proposed algorithm converges much faster than that of conventional superresolution image reconstruction method. By using the new method, the visual feeling of reconstructed image can be greatly improved compared to that of iterative back projection algorithm, and its peak signal-to-noise ratio can also be improved by nearly 1 dB higher than the projection onto convex sets algorithm. Furthermore, this method can successfully avoid the ill-posed problems in reconstruction process

Permutation invariance and uncertainty in multitemporal image super-resolution

Recent advances have shown how deep neural networks can be extremely effective at super-resolving remote sensing imagery, starting from a multitemporal collection of low-resolution images. However, existing models have neglected the issue of temporal permutation, whereby the temporal ordering of the input images does not carry any relevant information for the super-resolution task and causes such models to be inefficient with the, often scarce, ground truth data that available for training. Thus, models ought not to learn feature extractors that rely on temporal ordering. In this paper, we show how building a model that is fully invariant to temporal permutation significantly improves performance and data efficiency. Moreover, we study how to quantify the uncertainty of the super-resolved image so that the final user is informed on the local quality of the product. We show how uncertainty correlates with temporal variation in the series, and how quantifying it further improves model performance. Experiments on the Proba-V challenge dataset show significant improvements over the state of the art without the need for self-ensembling, as well as improved data efficiency, reaching the performance of the challenge winner with just 25% of the training data

Development Of A High Performance Mosaicing And Super-Resolution Algorithm

In this dissertation, a high-performance mosaicing and super-resolution algorithm is described. The scale invariant feature transform (SIFT)-based mosaicing algorithm builds an initial mosaic which is iteratively updated by the robust super resolution algorithm to achieve the final high-resolution mosaic. Two different types of datasets are used for testing: high altitude balloon data and unmanned aerial vehicle data. To evaluate our algorithm, five performance metrics are employed: mean square error, peak signal to noise ratio, singular value decomposition, slope of reciprocal singular value curve, and cumulative probability of blur detection. Extensive testing shows that the proposed algorithm is effective in improving the captured aerial data and the performance metrics are accurate in quantifying the evaluation of the algorithm

Super Resolution Imaging Needs Better Registration for Better Quality Results

In this paper, trade-off between effect of registration error and number of images used in the process of super resolution image reconstruction is studied. Super Resolution image reconstruction is three phase process, of which registration is of at most importance. Super resolution image reconstruction uses set of low resolution images to reconstruct high resolution image during registration. The study demonstrates the effects of registration error and benefit of more number of low resolution images on the quality of reconstructed image. Study reveals that the registration error degrades the reconstructed image and without better registration methodology, a better super resolution method is still not of any use. It is noticed that without further improvement in the registration technique, not much improvement can be achieved by increasing number of input low resolution images

Vedel-objektiiv abil salvestatud kaugseire piltide analüüs kasutades super-resolutsiooni meetodeid

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKäesolevas doktoritöös uuriti nii riist- kui ka tarkvaralisi lahendusi piltide töötlemiseks. Riist¬varalise poole pealt pakuti lahenduseks uudset vedelläätse, milles on dielekt¬rilisest elastomeerist kihilise täituriga membraan otse optilisel teljel. Doktoritöö käigus arendati välja kaks prototüüpi kahe erineva dielektrilisest elastomeerist ki¬hilise täituriga, mille aktiivne ala oli ühel juhul 40 ja teisel 20 mm. Läätse töö vas¬tas elastomeeri deformatsiooni mehaanikale ja suhtelistele muutustele fookuskau¬guses. Muutuste demonstreerimiseks meniskis ja läätse fookuskauguse mõõtmiseks kasutati laserkiirt. Katseandmetest selgub, et muutuste tekitamiseks on vajalik pinge vahemikus 50 kuni 750 volti. Tarkvaralise poole pealt pakuti uut satelliitpiltide parandamise süsteemi. Paku¬tud süsteem jagas mürase sisendpildi DT-CWT laineteisenduse abil mitmeteks sagedusalamribadeks. Pärast müra eemaldamist LA-BSF funktsiooni abil suu¬rendati pildi resolutsiooni DWT-ga ja kõrgsagedusliku alamriba piltide interpo¬leerimisega. Interpoleerimise faktor algsele pildile oli pool sellest, mida kasutati kõrgsagedusliku alamriba piltide interpoleerimisel ning superresolutsiooniga pilt rekonst¬rueeriti IDWT abil. Käesolevas doktoritöös pakuti tarkvaraliseks lahenduseks uudset sõnastiku baasil töötavat super-resolutsiooni (SR) meetodit, milles luuakse paarid suure resolutsiooniga (HR) ja madala resolut-siooniga (LR) piltidest. Kõigepealt jagati vastava sõnastiku loomiseks HR ja LR paarid omakorda osadeks. Esialgse HR kujutise saamiseks LR sisendpildist kombineeriti HR osi. HR osad valiti sõnastikust nii, et neile vastavad LR osad oleksid võimalikult lähedased sisendiks olevale LR pil¬dile. Iga valitud HR osa heledust korrigeeriti, et vähendada kõrvuti asuvate osade heleduse erine¬vusi superresolutsiooniga pildil. Plokkide efekti vähendamiseks ar¬vutati saadud SR pildi keskmine ning bikuupinterpolatsiooni pilt. Lisaks pakuti käesolevas doktoritöös välja kernelid, mille tulemusel on võimalik saadud SR pilte teravamaks muuta. Pakutud kernelite tõhususe tõestamiseks kasutati [83] ja [50] poolt pakutud resolutsiooni parandamise meetodeid. Superreso¬lutsiooniga pilt saadi iga kerneli tehtud HR pildi kombineerimise teel alpha blen¬dingu meetodit kasutades. Pakutud meetodeid ja kerneleid võrreldi erinevate tavaliste ja kaasaegsete meetoditega. Kvantita-tiivsetest katseandmetest ja saadud piltide kvaliteedi visuaal¬sest hindamisest selgus, et pakutud meetodid on tavaliste kaasaegsete meetoditega võrreldes paremad.In this thesis, a study of both hardware and software solutions for image enhance¬ment has been done. On the hardware side, a new liquid lens design with a DESA membrane located directly in the optical path has been demonstrated. Two pro¬totypes with two different DESA, which have a 40 and 20 mm active area in diameter, were developed. The lens performance was consistent with the mechan¬ics of elastomer deformation and relative focal length changes. A laser beam was used to show the change in the meniscus and to measure the focal length of the lens. The experimental results demonstrate that voltage in the range of 50 to 750 V is required to create change in the meniscus. On the software side, a new satellite image enhancement system was proposed. The proposed technique decomposed the noisy input image into various frequency subbands by using DT-CWT. After removing the noise by applying the LA-BSF technique, its resolution was enhanced by employing DWT and interpolating the high-frequency subband images. An original image was interpolated with half of the interpolation factor used for interpolating the high-frequency subband images, and the super-resolved image was reconstructed by using IDWT. A novel single-image SR method based on a generating dictionary from pairs of HR and their corresponding LR images was proposed. Firstly, HR and LR pairs were divided into patches in order to make HR and LR dictionaries respectively. The initial HR representation of an input LR image was calculated by combining the HR patches. These HR patches are chosen from the HR dictionary corre-sponding to the LR patches that have the closest distance to the patches of the in¬put LR image. Each selected HR patch was processed further by passing through an illumination enhancement processing order to reduce the noticeable change of illumination between neighbor patches in the super-resolved image. In order to reduce the blocking effect, the average of the obtained SR image and the bicubic interpolated image was calculated. The new kernels for sampling have also been proposed. The kernels can improve the SR by resulting in a sharper image. In order to demonstrate the effectiveness of the proposed kernels, the techniques from [83] and [50] for resolution enhance¬ment were adopted. The super-resolved image was achieved by combining the HR images produced by each of the proposed kernels using the alpha blending tech-nique. The proposed techniques and kernels are compared with various conventional and state-of-the-art techniques, and the quantitative test results and visual results on the final image quality show the superiority of the proposed techniques and ker¬nels over conventional and state-of-art technique

A Short Survey of Image Super Resolution Algorithms

Image super resolution is to estimate a high resolution image from a low resolution image or a sequence of low resolution images using image processing and machine learning technology. So far, there have emerged lots of super resolution algorithms. According to the input number of image, these algorithms can usually be divided as single image based algorithm and multiple images based algorithm. And according to technique principle, these algorithms can also be divided into three categories - interpolation based algorithm, reconstruction based algorithm and learning based one. This work mainly addresses the basic principle and different strategy of super resolution algorithms in detail. Then, the evaluation criteria and its application issues of super resolution are also discussed in the end

Super-Resolution Textured Digital Surface Map (DSM) Formation by Selecting the Texture From Multiple Perspective Texel Images Taken by a Low-Cost Small Unmanned Aerial Vehicle (UAV)

Textured Digital Surface Model (TDSM) is a three-dimensional terrain map with texture overlaid on it. Utah State University has developed a texel camera which can capture a 3D image called a texel image. A TDSM can be constructed by combining these multiple texel images, which is much cheaper than the traditional method. The overall goal is to create a TDSM for a larger area that is cheaper and equally accurate as the TDSM created using a high-cost system. The images obtained from such an inexpensive camera have a lot of errors. To create scientifically accurate TDSM, the error presented in the image must be corrected. An automatic process to create TDSM is presented that can handle a large number of input texel images. The advantage of using such a large set of input images is that they can cover a large area on the ground, making the algorithm suitable for large-scale applications. This is done by processing images and correcting them in a windowing manner. Furthermore, the appearance of the final 3D terrain map is improved by selecting the texture from many candidate images. This ensures that the best texture is selected. The selection criteria are discussed. Lastly, a method to increase the resolution of the final image is discussed. The methods described in this dissertation improve the current technique of creating TDSM, and the results are shown and analyzed

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

Evaluation of neural network based image super-resolution

Abstract. Super-resolution (SR) aims to produce a higher resolution image containing more details than the original image. The amount of pixels is easy to add with simple interpolation methods, but the amount of details does not increase. To overcome this limitation single image super-resolution (SISR) was introduced, which aims to recover the high-resolution (HR) image from the low-resolution (LR) images. Convolutional neural networks (CNN) have become an essential method in machine learning. With the growth of CNN, super-resolution solutions have grown immensely. In this work, a broad review is done on neural network methods designed for super-resolution. Four methods are chosen by their originality and different architectural choices, implemented in PyTorch framework. The models are already trained with public datasets, and the pre-trained models are used for the evaluation. The evaluation is done by analyzing the results with qualitative and quantitative methods. All the methods are tested with public datasets and a private dataset called Hiottu-1, including a wood surface images with different defect types. The evaluation is done based on their image quality and inference time. Peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) metrics are used for quality evaluation, and the inference time is measured by how fast the model generates the output result of test image. The chosen methods improved the image qualities of test images in each datasets. The best perfoming ones were swin image restoration (SwinIR) and pixel attention network (PAN) methods. SwinIR had better PSNR and SSIM values than PAN method and results were pealing to human eye. The inference time of SwinIR is slow, therefore the best possible application would be offline usage. The PAN method had impressing results and its inference time enables the real-time application usage. The SwinIR performed extremely well on Hiottu-1 dataset, with increasing the image quality of defect types and reducing noise overall. The PAN method got high metrics values on Hiottu-1 dataset, although the results were not as pealing as the SwinIR. In the wood manufacturing inspection side, the SwinIR could be utilized on slow production line with high defect detection accuracy, while the PAN method could be utilized on faster production line