Super Resolution Image Reconstruction for single image using Approximate BPTSRIRTD Algorithm

Super-resolution image reconstruction can achieve favorable feature extraction and image analysis. This study first investigated the image’s self-similarity and constructed high-resolution and low-resolution learning dictionaries; then, based on sparse representation and reconstruction algorithm in compressed sensing theory, super-resolution reconstruction (SRSR) of a single image was realized. The proposed algorithm adopted improved K-SVD algorithm for sample training and learning dictionary construction; additionally, the matching pursuit algorithm was improved for achieving single-image SRSR based on image’s self-similarity and compressed sensing. The experimental results reveal that the proposed reconstruction algorithm shows better visual effect and image quality than the degraded low-resolution image; moreover, compared with the reconstructed images using bilinear interpolation and sparse-representation-based algorithms, the reconstructed image using the proposed algorithm has a higher PSNR value and thus exhibits more favorable super-resolution image reconstruction performance.

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Single Image Reconstruction using Novel Super Resolution Technique for Large Scaled Images

10.21203/rs.3.rs-1047644/v1 ◽

2021 ◽

Author(s):

Ramanath Datta ◽

Sekhar Mandal ◽

Saiyed Umer ◽

Ahmad Ali AlZubi ◽

Abdullah Alharbi ◽

...

Keyword(s):

Image Reconstruction ◽

Architectural Design ◽

State Of The Art ◽

Super Resolution ◽

Electrical Machine ◽

Single Image ◽

Resolution Image ◽

Working Principle ◽

Novel Method ◽

Homogeneous Regions

Abstract A fast and novel method for single-image reconstruction using super resolution (SR) technique has been proposed in this paper. The working principle of proposed technique has been divided into three components. In the first component, a low resolution image is divided into several homogeneous or non-homogeneous regions. This partition is based on the analysis of texture pattern within that region. Only the non-homogeneous regions undergo to the sparse representation for super resolution image reconstruction in the second component. The obtained reconstructed region from the second component undergoes to a statistical based prediction model to generate its more enhanced version in the third component. The remaining homogeneous regions are bicubic interpolated and reflected to the required high resolution image. The proposed technique is applied on some Large scaled Electrical, Machine and Civil architectural design images. The purpose of using these images is that these images are huge in size and processing such large images for any applications, is time consuming. The proposed SR technique results the better reconstructed SR image from its very lower version with low time complexity. The performance of the proposed system on the Electrical, Machine and Civil architectural design images is compared with the state-of-the-art methods and it is shown that the proposed system outperforms the other competing methods.

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