58 research outputs found
Development of Image Super-resolution Algorithms
This thesis addresses the problem of image super-resolution from a low resolution image. Spatial resoltuion of images are restricted by the size of CMOS sensors. Spatial resolution can be increased by increasing no of COMS sensors resuling in decrease in size of CMOS sensors which cause shot noise. In this thesis attempts have been made to enhance the spatial resolution of different images. Two schemes are proposed for this purpose. The basic idea behind both the techniques is to utilize the high frequency subband images derived using lifting wavelet transform. In the first scheme the high frequency subband images are interpolated using surface fitting. In another scheme lifting wavelet transform and stationary wavelet transform are used along with surface fitting interpolation to increase the spatial resolution in the frequency domain. Each technique is studied separately, and experiments are conducted to evaluate their performances. The visual, blind image quality index, visual image fidelity index as well as the peak signal to noise ratio (PSNR in dB) of high resolution images are compared with competent recent schemes. Experimental results demonstrate that the proposed approaches are very effective in increasing resolution and compare favorably to state-of-the-art super-resolution algorithms
Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels
The deep learning technique was used to increase the performance of single
image super-resolution (SISR). However, most existing CNN-based SISR approaches
primarily focus on establishing deeper or larger networks to extract more
significant high-level features. Usually, the pixel-level loss between the
target high-resolution image and the estimated image is used, but the neighbor
relations between pixels in the image are seldom used. On the other hand,
according to observations, a pixel's neighbor relationship contains rich
information about the spatial structure, local context, and structural
knowledge. Based on this fact, in this paper, we utilize pixel's neighbor
relationships in a different perspective, and we propose the differences of
neighboring pixels to regularize the CNN by constructing a graph from the
estimated image and the ground-truth image. The proposed method outperforms the
state-of-the-art methods in terms of quantitative and qualitative evaluation of
the benchmark datasets.
Keywords: Super-resolution, Convolutional Neural Networks, Deep Learnin
Super-resolution reconstruction of brain magnetic resonance images via lightweight autoencoder
Magnetic Resonance Imaging (MRI) is useful to provide detailed anatomical information such as images of tissues and organs within the body that are vital for quantitative image analysis. However, typically the MR images acquired lacks adequate resolution because of the constraints such as patients’ comfort and long sampling duration. Processing the low resolution MRI may lead to an incorrect diagnosis. Therefore, there is a need for super resolution techniques to obtain high resolution MRI images. Single image super resolution (SR) is one of the popular techniques to enhance image quality. Reconstruction based SR technique is a category of single image SR that can reconstruct the low resolution MRI images to high resolution images. Inspired by the advanced deep learning based SR techniques, in this paper we propose an autoencoder based MRI image super resolution technique that performs reconstruction of the high resolution MRI images from low resolution MRI images. Experimental results on synthetic and real brain MRI images show that our autoencoder based SR technique surpasses other state-of-the-art techniques in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Information Fidelity Criterion (IFC), and computational time
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
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