scholarly journals A No-Reference CNN-Based Super-Resolution Method for KOMPSAT-3 Using Adaptive Image Quality Modification

2021 ◽  
Vol 13 (16) ◽  
pp. 3301
Author(s):  
Yeonju Choi ◽  
Sanghyuck Han ◽  
Yongwoo Kim
Keyword(s):  
Image Quality ◽  
Satellite Images ◽  
Satellite Image ◽  
Super Resolution ◽  
Ground Truth ◽  
Perceptual Quality ◽  
High Quality ◽  
Degradation Model ◽  
Sr Method

In recent years, research on increasing the spatial resolution and enhancing the quality of satellite images using the deep learning-based super-resolution (SR) method has been actively conducted. In a remote sensing field, conventional SR methods required high-quality satellite images as the ground truth. However, in most cases, high-quality satellite images are difficult to acquire because many image distortions occur owing to various imaging conditions. To address this problem, we propose an adaptive image quality modification method to improve SR image quality for the KOrea Multi-Purpose Satellite-3 (KOMPSAT-3). The KOMPSAT-3 is a high performance optical satellite, which provides 0.7-m ground sampling distance (GSD) panchromatic and 2.8-m GSD multi-spectral images for various applications. We proposed an SR method with a scale factor of 2 for the panchromatic and pan-sharpened images of KOMPSAT-3. The proposed SR method presents a degradation model that generates a low-quality image for training, and a method for improving the quality of the raw satellite image. The proposed degradation model for low-resolution input image generation is based on Gaussian noise and blur kernel. In addition, top-hat and bottom-hat transformation is applied to the original satellite image to generate an enhanced satellite image with improved edge sharpness or image clarity. Using this enhanced satellite image as the ground truth, an SR network is then trained. The performance of the proposed method was evaluated by comparing it with other SR methods in multiple ways, such as edge extraction, visual inspection, qualitative analysis, and the performance of object detection. Experimental results show that the proposed SR method achieves improved reconstruction results and perceptual quality compared to conventional SR methods.

scholarly journals Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

2021 ◽  
Vol 13 (4) ◽  
pp. 606
Author(s):  
Tee-Ann Teo ◽  
Yu-Ju Fu
Keyword(s):  
Satellite Images ◽  
Traditional Approach ◽  
Spectral Band ◽  
Super Resolution ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fusion Model ◽  
Image Blending ◽  
Fine Resolution

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.

scholarly journals Implementasi Metode Super Resolusi Untuk Meningkatkan Kualitas Citra Hasil Screenshot

2020 ◽  
Vol 7 (3) ◽  
pp. 432
Author(s):  
Windi Astuti
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Median Filter ◽  
Super Resolution ◽  
Linear Interpolation ◽  
Resolution Method ◽  
Image Zooming ◽  
Image Enlargement ◽  
Three Stages

Various types of image processing that can be done by computers, such as improving image quality is one of the fields that is quite popular until now. Improving the quality of an image is necessary so that someone can observe the image clearly and in detail without any disturbance. An image can experience major disturbances or errors in an image such as the image of the screenshot is used as a sample. The results of the image from the screenshot have the smallest sharpness and smoothness of the image, so to get a better image is usually done enlargement of the image. After the screenshot results are obtained then, the next process is cropping the image and the image looks like there are disturbances such as visible blur and cracked. To get an enlarged image (Zooming image) by adding new pixels or points. This is done by the super resolution method, super resolution has three stages of completion, first Registration, Interpolation, and Reconstruction. For magnification done by linear interpolation and reconstruction using a median filter for image refinement. This method is expected to be able to solve the problem of improving image quality in image enlargement applications. This study discusses that the process carried out to implement image enlargement based on the super resolution method is then built by using R2013a matlab as an editor to edit programs

Perceptual Quality Assessment of Low-light Image Enhancement

2021 ◽  
Vol 17 (4) ◽  
pp. 1-24
Author(s):  
Guangtao Zhai ◽  
Wei Sun ◽  
Xiongkuo Min ◽  
Jiantao Zhou
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Perceptual Quality ◽  
Low Light ◽  
Light Image ◽  
Light Enhancement

Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.

scholarly journals Generative Adversarial Network-Based Super-Resolution Considering Quantitative and Perceptual Quality

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 449 ◽  
Author(s):  
Can Li ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Naixiang Ao
Keyword(s):  
Super Resolution ◽  
Ground Truth ◽  
Second Order ◽  
Attention Mechanism ◽  
Generative Adversarial Networks ◽  
Perceptual Quality ◽  
Human Visual Perception ◽  
Image Perception ◽  
Generative Adversarial Network ◽  
Image Super Resolution

In recent years, the common algorithms for image super-resolution based on deep learning have been increasingly successful, but there is still a large gap between the results generated by each algorithm and the ground-truth. Even some algorithms that are dedicated to image perception produce more textures that do not exist in the original image, and these artefacts also affect the visual perceptual quality of the image. We believe that in the existing perceptual-based image super-resolution algorithm, it is necessary to consider Super-Resolution (SR) image quality, which can restore the important structural parts of the original picture. This paper mainly improves the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) algorithm in the following aspects: adding a shallow network structure, adding the dual attention mechanism in the generator and the discriminator, including the second-order channel mechanism and spatial attention mechanism and optimizing perceptual loss by adding second-order covariance normalization at the end of feature extractor. The results of this paper ensure image perceptual quality while reducing image distortion and artefacts, improving the perceived similarity of images and making the images more in line with human visual perception.

scholarly journals Interobserver variability in quality assessment of magnetic resonance images

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Rafal Obuchowicz ◽  
Mariusz Oszust ◽  
Adam Piorkowski
Keyword(s):  
Magnetic Resonance ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Interobserver Variability ◽  
Poor Performance ◽  
Magnetic Resonance Images ◽  
Perceptual Quality ◽  
Mr Images

Abstract Background The perceptual quality of magnetic resonance (MR) images influences diagnosis and may compromise the treatment. The purpose of this study was to evaluate how the image quality changes influence the interobserver variability of their assessment. Methods For the variability evaluation, a dataset containing distorted MRI images was prepared and then assessed by 31 experienced medical professionals (radiologists). Differences between observers were analyzed using the Fleiss’ kappa. However, since the kappa evaluates the agreement among radiologists taking into account aggregated decisions, a typically employed criterion of the image quality assessment (IQA) performance was used to provide a more thorough analysis. The IQA performance of radiologists was evaluated by comparing the Spearman correlation coefficients, ρ, between individual scores with the mean opinion scores (MOS) composed of the subjective opinions of the remaining professionals. Results The experiments show that there is a significant agreement among radiologists (κ=0.12; 95% confidence interval [CI]: 0.118, 0.121; P<0.001) on the quality of the assessed images. The resulted κ is strongly affected by the subjectivity of the assigned scores, separately presenting close scores. Therefore, the ρ was used to identify poor performance cases and to confirm the consistency of the majority of collected scores (ρmean = 0.5706). The results for interns (ρmean = 0.6868) supports the finding that the quality assessment of MR images can be successfully taught. Conclusions The agreement observed among radiologists from different imaging centers confirms the subjectivity of the perception of MR images. It was shown that the image content and severity of distortions affect the IQA. Furthermore, the study highlights the importance of the psychosomatic condition of the observers and their attitude.

Generating High-Quality Air-Coupled Ultrasonic Images for Wooden Material Characterization by Single Image Super-Resolution

2019 ◽  
Vol 34 (03) ◽  
pp. 2054008
Author(s):  
Lujun Lin ◽  
Yiming Fang ◽  
Xiaochen Du ◽  
Zhu Zhou
Keyword(s):  
Image Quality ◽  
Super Resolution ◽  
Visual Assessment ◽  
Wood Products ◽  
Superior Performance ◽  
Single Image ◽  
High Quality ◽  
Practical Applications ◽  
Image Super Resolution ◽  
Single Image Super Resolution

As the practical applications in other fields, high-resolution images are usually expected to provide a more accurate assessment for the air-coupled ultrasonic (ACU) characterization of wooden materials. This paper investigated the feasibility of applying single image super-resolution (SISR) methods to recover high-quality ACU images from the raw observations that were constructed directly by the on-the-shelf ACU scanners. Four state-of-the-art SISR methods were applied to the low-resolution ACU images of wood products. The reconstructed images were evaluated by visual assessment and objective image quality metrics, including peak signal-to-noise-ratio and structural similarity. Both qualitative and quantitative evaluations indicated that the substantial improvement of image quality can be yielded. The results of the experiments demonstrated the superior performance and high reproducibility of the method for generating high-quality ACU images. Sparse coding based super-resolution and super-resolution convolutional neural network (SRCNN) significantly outperformed other algorithms. SRCNN has the potential to act as an effective tool to generate higher resolution ACU images due to its flexibility.

Evaluation of the sparse coding super-resolution method for improving image quality of up-sampled images in computed tomography

2017 ◽  
Author(s):  
Junko Ota ◽  
Kensuke Umehara ◽  
Naoki Ishimaru ◽  
Shunsuke Ohno ◽  
Kentaro Okamoto ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Sparse Coding ◽  
Super Resolution ◽  
Resolution Method

scholarly journals Studies on the effect of MegaPixel sensor resolution on displayed image quality and relevant metrics

2020 ◽  
Vol 2020 (9) ◽  
pp. 170-1-170-10
Author(s):  
Sophie Triantaphillidou ◽  
Jan Smejkal ◽  
Edward W. S. Fry ◽  
Chuang Hsin Hung
Keyword(s):  
Image Quality ◽  
Consumer Experience ◽  
Camera Phone ◽  
High Quality ◽  
Subjective Image Quality ◽  
Star Rating ◽  
Quality Ratings ◽  
Large Numbers ◽  
Quality Scale

This paper investigates camera phone image quality, namely the effect of sensor megapixel (MP) resolution on the perceived quality of images displayed at full size on high-quality desktop displays. For the purpose, we use images from simulated cameras with different sensor MP resolutions. We employ methods recommended in the IEEE 1858 Camera Phone Image Quality (CPIQ) standard, as well as other established psychophysical paradigms, to obtain subjective image quality ratings for systems with varying MP resolution from large numbers of observers. These are subsequently used to validate image quality metrics (IQMs) relating to sharpness and resolution, including those from the CPIQ standard. Further, we define acceptable levels of quality - when changing MP resolution - for mobile phone images in Subjective Quality Scale (SQS) units. Finally, we map SQS levels to categories obtained from star-rating experiments (commonly used to rate consumer experience). Our findings draw a relationship between the MP resolution of the camera sensor and the LCD device. The chosen metrics predict quality accurately, but only the metrics proposed by CPIQ return results in calibrated JNDs in quality. We close by discussing the appropriateness of star-rating experiments for the purpose of measuring subjective image quality and metric validation.

scholarly journals NO-REFERENCE IMAGE QUALITY ASSESSMENT FOR ZY3 IMAGERY IN URBAN AREAS USING STATISTICAL MODEL

2016 ◽  
Vol XLI-B3 ◽  
pp. 949-954 ◽  
Author(s):  
Y. Zhang ◽  
W.H. Cui ◽  
F. Yang ◽  
Z.C. Wu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Urban Areas ◽  
Satellite Images ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Atmospheric Conditions ◽  
Natural Scene Statistics ◽  
The Impact

More and more high-spatial resolution satellite images are produced with the improvement of satellite technology. However, the quality of images is not always satisfactory for application. Due to the impact of complicated atmospheric conditions and complex radiation transmission process in imaging process the images often suffer deterioration. In order to assess the quality of remote sensing images over urban areas, we proposed a general purpose image quality assessment methods based on feature extraction and machine learning. We use two types of features in multi scales. One is from the shape of histogram the other is from the natural scene statistics based on Generalized Gaussian distribution (GGD). A 20-D feature vector for each scale is extracted and is assumed to capture the RS image quality degradation characteristics. We use SVM to learn to predict image quality scores from these features. In order to do the evaluation, we construct a median scale dataset for training and testing with subjects taking part in to give the human opinions of degraded images. We use ZY3 satellite images over Wuhan area (a city in China) to conduct experiments. Experimental results show the correlation of the predicted scores and the subjective perceptions.

scholarly journals Contenido y calidad de las imágenes de observación terrestre

2019 ◽  
pp. 15-21
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Information Extraction ◽  
Satellite Images ◽  
Satellite Image ◽  
La Paz ◽  
Sensing System ◽  
Manual Intervention ◽  
Shape Structure ◽  
Important Amount

Contenido y calidad de las imágenes de observación terrestre Earth observation image information content and quality Avid Roman-Gonzalez, Natalia Indira Vargas-Cuentas TELECOM ParisTech, 46 rue Barrault, 75013 – Paris, Francia Escuela Militar de Ingeniería – EMI, La Paz, Bolivia DOI: https://doi.org/10.33017/RevECIPeru2012.0015/ Resumen En el presente artículo describiremos la extracción de información de imágenes satelitales y la importancia de la calidad de las imágenes satelitales. Indagaremos con más detalle en el ámbito de los artefactos y su influencia en la extracción de información de las imágenes satelitales. En un sistema de teledetección, si bien, las imágenes son muy importantes, pero lo más importante es la información que podemos extraer de ellas para interpretar y aplicar esta información en diferentes campos. En ese sentido, la calidad de imagen juega un papel importante. Si queremos obtener la mayor e importante cantidad de información de una imagen, es necesario que la imagen tenga una buena calidad. El principal objetivo de cualquier sistema de teledetección es el uso de la información que se puede extraer de las imágenes, esto incluye la detección, medición, identificación e interpretación de diferentes objetivos de interés. Los objetivos de interés en imágenes de teledetección pueden ser cualquier característica, objeto, textura, forma, estructura, espectro o cobertura superficial que están en la imagen. El proceso de un sistema de teledetección y análisis puede ser realizado manualmente o de manera automática, en realidad, hay muchos grupos de investigación que desarrollan diferentes herramientas para detectar, identificar, interpretar y extraer información de los objetivos de interés sin intervención manual de un intérprete humano. Descriptores: teledetección, imágenes satelitales, detección de artefactos, calidad de las imágenes. Abstract In this article we will describe the information extraction from satellite image, the importance of image quality in satellite image. In this paper we will study in more detail the artifacts and their influence on the information extraction from satellite images. In a remote sensing system, although, the images are very important, but more important is the information that we can extract from them to interpret and apply this information in different fields. In this sense, the image quality plays an important role. If we want to get the biggest and most important amount of information from the image, we need to have a good image quality. The main objective of any remote sensing system is the use of information that we can extract from the images, this includes detection, measurement, identification and interpretation of different targets. Targets in remote sensing images may be any feature, object, texture, shape, structure, spectrum or land covers which are in the image. Remote sensing process and analysis could be performed manually or automatically, actually, there are many research groups that develop different tools for detect, identify, extract information and interpret targets without manual intervention by a human interpreter. Keywords: remote sensing, satellite images, artifacts detection, image quality.

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