Medical Image Enhancement Using Super Resolution Methods

Super resolution (SR) techniques play a critical role in enhancing the quality of satellite imagery by reconstructing high resolution (HR) images from low resolution (LR) inputs. This study investigates the application of the Enhanced Super Resolution Generative Adversarial Network (ESRGAN) to improve the spatial resolution of satellite images. The ESRGAN framework integrates a Residual-in-Residual Dense Block (RRDB) generator, a Relativistic average GAN (RaGAN) discriminator, and an improved perceptual loss function to achieve sharper textures and more realistic structures. The 4× Satellite Image Super Resolution dataset, consisting of paired LR (2 m/pixel) and HR (0.5 m/pixel) images, was employed. Quantitative evaluation was conducted using the Peak Signal-to-Noise Ratio (PSNR), with Lanczos5 interpolation used as a baseline for comparison. Experimental results on ten test images demonstrate that ESRGAN consistently outperforms Lanczos5. Qualitative assessments further confirm that ESRGAN effectively restores high frequency details, enhances fine textures, and preserves object boundaries more accurately than the interpolation method. These findings highlight ESRGAN’s potential as an advanced solution for improving visual quality and detail preservation in high resolution satellite imagery applications.

Low-resolution medical images can seriously interfere with the medical diagnosis, and poor image quality can lead to loss of detailed information. Therefore, improving the quality of medical images and accelerating the reconstruction is of particular importance for diagnosis. To solve this problem, we propose a wavelet-based mini-grid network medical image super-resolution (WMSR) method, which is similar to the three-layer hidden-layer-based super-resolution convolutional neural network (SRCNN) method. Due to the amplification characteristics of wavelets, a stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT). Also, due to the nature of redundant (scale-by-scale) wavelets, it is possible to retain additional information about the image and restore high-resolution images in detail. For a large amount of training data, wavelet sub-band images, including approximation and frequency subbands are combined into a predefined full-scale factor. The mapping between the wavelet sub-band image and its approximate image is then determined. In order to ensure the reproducibility of the image, a method of adding a sub-pixel layer is proposed to realize the hidden layer, and replacing the small mini-grid-network on the hidden layer is of considerable significance to speed up the image recovery speed. Experimental results on the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) show that the model has better performance.

This paper presents a new quality assessment parameter for the evaluation of deep learning based super resolution techniques applied on thermal images. Three widely used deep learning-based models namely Super-Resolution Convolutional Neural Network (SRCNN), Thermal Enhancement Network (TEN) and Very Deep Super Resolution (VDSR) have been implemented for achieving super resolution of thermal images. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) have been the most widely used conventional evaluation metrics for performance measurement of super resolution algorithms. Since these parameters require a reference image for the evaluation of the resultant images, we propose a new quality assessment parameter based on strength of the edges. Edge detection of the super resoluted image is performed utilizing Canny Edge Detection method and the entropy of the edge detection image is computed to provide a new parameter, Edge Detection Entropy Score (EDES). For the comparison and validation of the proposed image quality assessment techniques, Mean Opinion Score (MOS) of the target images have been obtained to be used as a benchmark. The obtained results indicate that the proposed EDES of the super resoluted images has high degree of correlation with the MOS as well as PSNR and SSIM.

Magnetic Resonance Imaging (MRI) provides a robust, non-invasive method for brain assessment and the detection of age-related biomarkers. However, due to hardware restrictions or the need for shorter acquisition times, clinical scans are often acquired at reduced spatial resolution, leading to degraded image quality that hampers tasks such as age prediction and other downstream analyses. To address this challenge, we propose a lightweight two-stage deep learning framework that combines super-resolution reconstruction with age regression. In the first stage, a Super-Resolution Convolutional Neural Network (SRCNN) enhances low-quality axial brain MRI slices, recovering fine structural details and improving overall image fidelity. The reconstructed images achieve a peak signal-to-noise ratio (PSNR) of 30.45 dB, a mean squared error (MSE) of 0.0010, and a structural similarity index (SSIM) of 0.9599 on the enhanced dataset. In the second stage, a specialized convolutional neural network leverages the super-resolved images to estimate biological age. Tested on the IXI dataset, the model demonstrates strong predictive accuracy, achieving a mean absolute error (MAE) of 2.35 years, a root mean squared error (RMSE) of 3.05 years, and a coefficient of determination (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex>) of 0.965. These results highlight the effectiveness of super-resolution as a preprocessing step for improving regression performance. The proposed lightweight and interpretable architecture illustrates the advantages of integrating high-fidelity reconstruction with predictive modeling in a unified pipeline, enabling improved brain MRI analysis and reliable age prediction.

Image super-resolution (SR) can be seen as finding a nonlinear mapping between low-resolution (LR) image and high-resolution (HR) image. Convolutional neural networks (CNN) based image SR methods have achieved great success based on features extracted from pretrained networks. However, the previous works less consider multiscale features. In this paper, we propose a SR cascaded multi-column convolutional neural network (SRCMCN) to recover HR image by learning multi-scale features from the corresponding LR image. Compared to existing CNN based image SR methods, the proposed method directly takes the original LR image as input to learn the residual between interpolated LR image and corresponding HR image, which will reduce the computational cost of convolution due to the small input size. Besides, in order to achieve high-quality reconstruction, we optimize our networks using mean absolute error (MAE) loss function. Experimental results demonstrate that our proposed method outperforms several state-of-the-art image SR methods in terms of both reconstructed image quality and computational efficiency.

Low-resolution images can be reconstructed into high-resolution images using the Super-resolution Convolution Neural Network (SRCNN) algorithm. This study aims to improve the vehicle license plate number's recognition accuracy by generating a high-resolution vehicle image using the SRCNN. The recognition is carried out by two types of character recognition methods: Tesseract OCR and SPNet. The training data for SRCNN uses the DIV2K dataset consisting of 900 images, while the training data for character recognition uses the Chars74 dataset. The high-resolution images constructed using SRCNN can increase the average accuracy of vehicle license plate number recognition by 16.9 % using Tesseract and 13.8 % with SPNet.

Deep convolutional neural networks (CNNs), trained on corresponding pairs of high- and low-resolution images, achieve state-of-the-art performance in single-image super- resolution and surpass previous signal-processing based approaches. However, their performance is limited when applied to real photographs. The reason lies in their train- ing data: low-resolution (LR) images are obtained by bicu- bic interpolation of the corresponding high-resolution (HR) images. The applied convolution kernel significantly differs from real-world camera-blur. Consequently, while current CNNs well super-resolve bicubic-downsampled LR images, they often fail on camera-captured LR images. To improve generalization and robustness of deep super- resolution CNNs on real photographs, we present a ker- nel modeling super-resolution network (KMSR) that incor- porates blur-kernel modeling in the training. Our pro- posed KMSR consists of two stages: we first build a pool of realistic blur-kernels with a generative adversarial net- work (GAN) and then we train a super-resolution network with HR and corresponding LR images constructed with the generated kernels. Our extensive experimental vali- dations demonstrate the effectiveness of our single-image super-resolution approach on photographs with unknown blur-kernels.

Super-resolution reconstruction of knee magnetic resonance imaging based on deep learning

Secure transmission of images have become an important in case of its application and research. This study proposes a discrete wavelet-based resolution synthesis using interpolation in discrete wavelet transform (DWT) with a novel encryption method. Highly resoluted image has its importance in various applications like radar, electron microscopy, digital X-ray mammography, optical coherence tomography, astronomy, etc. High-resolution image is recovered from a low-resolution image where the image is transformed using Cohen-Daubechies-Feauveau 9/7 wavelet to obtain significant effectiveness in image resolution. By using the DWT, low-quality image is divided into various subbands of different frequency levels. These low-resoluted images are then interpolated using Cohen-Daubechies wavelet 9/7 coefficients and this interpolation provides nonlinearity over the image in order to preserve the visual smoothness edges. Preserved subband images are projected using fanbeam projection. These preserved various level images are used for reconstruction. High-resoluted image is then obtained using inverse discrete wavelet transform. These images are encrypted using the Pascal series. A significant improvement is obtained over other 2-D wavelet decomposition techniques such as bilinear, bicubic, nearest neighbor, and box wavelets. This study also implements this technique with the Digital Imaging and Communications in Medicine (DICOM) images for the betterment of performance metrics like peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These results are produced using MATLAB simulation software.

&lt;p&gt;Pneumonia is a severe lung infection, chest X-ray (CXR) image preferred to find infection. Real images lost its quality, resolution and other feature due to transmission. So good qualitative datasets are very limited. Quality enhancement in medical images is challenging task for researchers. And quality in clinical diagnosis of any disease in deep learning play a very important role. So, this paper presents an aspect with importance of quality in medical images CXR of a particular dataset and how to enhance and create new images with high quality resolution, that is re-used for classification in deep learning. Super resolution convolutional neural netwok (SRCNN) is deep learning based method, which is used for improving resolution in image. Super resolution means low resolution (LR) images from dataset is to be reconstructed or magnified into high resolution (HR). The objective behind this study is to measure the effect of super resolution with quality index, peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index measure (SSIM). This experinment performed on 200 images with 10 batches, each batch has 20 images from Kermany dataset, select LR images and converted into HR with SRCNN. Then we find PSNR value of image is increase upto 2 to 5 DB, and MSE of qood quality images is near to zero and MSE decrease up to 20-25, SSIM value have little variation due to same pattern is found in input and output images. Enhancement means highlight or improve the region of interest of pneumonic images. Main goal of this study is to preapare a modified dataset which is further used for classification.&lt;/p&gt;

Tropospheric Black Carbon (BC) aerosols are short lived positive radiative forcer with critical impacts on cardiovascular and pulmonary health. The MRI-ESM2(CMIP6) delivers the BC monthly global surface concentration from 1950-2100 at coarse special resolution of 1.875° × 1.875° which does not capture the city level BC hotspots at the global scale. The BC hotspots are essential for reducing the BC aerosols induced health burden, implementing air quality management policies and regional planning. The downscaling algorithm is an enhanced U-net with attention-based convolution neural network (Super Resolution Convolution Neural Network (SRCNN)). The SRCNN model executes downscaling of MRI-ESM2(CMIP6) BC monthly surface concentration with special resolution of 1.875° × 1.875° to NASA’s MERRA2 reanalysis BC monthly average surface concentration at a spatial resolution of 0.5° × 0.625°, thus achieving 3.6 times downscaling for identification city level BC-hotspots and cold spots at a global scale. The SRCNN model is trained on global monthly average BC surface concentration data from MRI-ESM2(CMIP6) and NASA’s MERRA2 reanalysis product. The model training is spread from 1980-2012(31 years) with validation from 2013-2016(4 years) and testing for 2017-2020(4 years). We have also examined the effects of Channel Based Attention Module (CBAM) with and without Residual Block (RB) and their effectiveness and efficacy in climate data downscaling with data-scarce condition. The training results showes that CBAM with RB based CNN outperforms then both (CNN without CBAM and without CBAM &amp; RB) in the benchmarks such as stability, overfitting, validation losses etc. The training results for SRCNN (with CBAM and RB) shows a final validation losses of 0.0028, final R² value of 0.7162, final Pearson-r value of 0.8467 with Structural Similarity Index Measure (SSIM) at 0.9954. The SRCNN (with CBAM &amp; RB) model testing reveals it performs exceptionally well in the identification of hotspots and cold-spots, with final testing RSME at 0.0015, final R2 at 0.88, final Pearson-r values at 0.94 and final SSIM at 0.99. Furthermore, testing outputs of SRCNN with attention module and residual blocks shows close fidelity with MERRA2 reanalysis vis-à-vis MRI-ESM2(CMIP6) at both seasonal and annual temporal resolution thus reducing systematic bias between ground truth and global climate models.

Super-resolution (SR) can be defined as a method of reconstructing high resolution(HR) image by increasing spatial resolution of one or more low resolution(LR) image counterparts. SR is widely applied in areas like satellite imaging, drones, surveillance and medical imaging etc. SR techniques can be broadly classified as external database driven or internal database driven approaches. The first approach is time consuming during training phase, in learning LR-HR patch relationship but computes fast during testing phase. The second approach directly constructs the super resolved image from existing LR image without the need of any training procedures but found to be computationally intensive during test time. This paper focuses on reducing the computation time of transformed self exemplar based internal database driven technique [1] that uses 3D scene geometry and internal image characteristics for super resolution by exploiting its highly parallelizable nature. On parallelizing the original algorithm we achieved a speedup of 6 on multi-core, upto 10 on GPU, around 14 on hybrid platform of multi-core and GPU. Using our hybrid implementation we can achieve 32x super resolution factor. We compare our results with SRCNN[2] (Super Resolution Convolution Neural Network) an external database SR technique which has a very good performance among the current start-of-the-art SR methods.

To reduce external disturbances and achieve high vertical resolution, the scanning time for white-light interference microscopy is very short. Because capturing high-resolution (HR) images is time consuming, low-resolution (LR) images are acquired instead. However, HR images are more desirable because they contain more details. To ensure high vertical resolution and high image resolution, one feasible solution is to process the scanned LR images to HR images by single image super-resolution (SISR). In this paper, an interference image super-resolution (IISR) model based on a generative adversarial network (GAN) is proposed. The generator is based on the enhanced super-resolution generative adversarial network (ESRGAN) architecture. With the aim of acquiring more realistic images, the discriminator network is designed using a modified DenseNet architecture, in which the pooling layers are replaced with dilated convolutional layers. The perceptual loss is optimized, and the content loss is upgraded to a continuously differentiable piecewise function. Various microscopy images are tested, including images with and without interference fringes. The IISR model has been proven to restore LR images to HR images. The comparative experiments prove that the proposed model achieves better visual quality than other models, preserving more realistic details.

In this paper, we investigate a convolutional neural network (CNN) approach for light field (LF) super-resolution (SR). We are motivated by the assumption that image priors can be embedded into CNN, and both external and internal correlations are important in LFSR. The LF images are indeed natural images except for its angular resolution, so the external correlations help to super-resolve a single image from a collection of general images, whilst the internal correlations are essential to enhance a single view in LF with the details in the other views. Accordingly, we propose a two-stage CNN, where the two stages exploit the external and internal correlations, respectively. Moreover, to improve the generalization ability of the second-stage CNN for inter-view SR, we propose to align different views at patch level to compensate for the disparity that is essential to LFSR, thus the second stage is termed multi-patch fusion CNN. Experimental results demonstrate the superior performance of our two-stage CNN compared with the state-of-the-art CNN-based SR methods.

Normalized difference vegetation index (NDVI) carries valuable information related to the photosynthetic activity of vegetation and is essential for monitoring phenological changes and ecosystem dynamics. The medium to high spatial resolution satellite images from Landsat 8 and Sentinel 2 offer opportunities to generate dense NDVI time series at 10-m resolution to improve our understanding of the land surface processes. However, synergistic use of Landsat 8 and Sentinel 2 for generating frequent and consistent NDVI data remains challenging as they have different spatial resolutions and spectral response functions. In this letter, we developed an attentional super resolution convolutional neural network (ASRCNN) for producing 10-m NDVI time series through fusion of Landsat 8 and Sentinel 2 images. We evaluated its performance in two heterogeneous areas. Quantitative assessments indicated that the developed network outperforms five commonly used fusion methods [i.e., enhanced deep convolutional spatiotemporal fusion network (EDCSTFN), super resolution convolutional neural network (SRCNN), spatial and temporal adaptive reflectance fusion model (STARFM), enhanced STARFM (ESTARFM), and flexible spatiotemporal data fusion (FSDAF)]. The influence of the method selection on the fusion accuracy is much greater than that of the fusion strategy in blending Landsat-Sentinel NDVI. Our results illustrate the advantages and potentials of the deep learning approaches on satellite data fusion.