Abstract

It is difficult to improve image resolution in hardware due to the limitations of technology and too high costs, but most application fields need high resolution images, so super-resolution technology has been produced. This paper mainly uses information redundancy to realize multi-frame super-resolution. In recent years, many researchers have proposed a variety of multi-frame super-resolution methods, but it is very difficult to preserve the image edge and texture details and remove the influence of noise effectively in practical applications. In this paper, a minimum variance method is proposed to select the low resolution images with appropriate quality quickly for super-resolution. The half-quadratic function is used as the loss function to minimize the observation error between the estimated high resolution image and low-resolution images. The function parameter is determined adaptively according to observation errors of each low-resolution image. The combination of a local structure tensor and Bilateral Total Variation (BTV) as image prior knowledge preserves the details of the image and suppresses the noise simultaneously. The experimental results on synthetic data and real data show that our proposed method can better preserve the details of the image than the existing methods.

Highlights

  • Super Resolution (SR) refers to the process of reconstructing a High Resolution (HR)image from a single Low Resolution (LR) image or multiple low resolution images by the software method without modifying the hardware environment

  • We need a simple and effective method to quickly select appropriate LR images, so we propose a method of minimum variance

  • We find that the half-quadratic function to the L2 norm when observation errors are small and it is close to the L1 norm when is close to the L2errors normare when observation errors are small andfunction, it is close to the

Read more

Summary

Introduction

Super Resolution (SR) refers to the process of reconstructing a High Resolution (HR). In recent many researchers large number of MFSR methods, image because the result of is affected by motion estimation, image registration, but MFSR is an ill-posed problem and it is very difficult to reconstruct a satisfactory HR unknown blur, noise, and soofforth. Even if they areby studied separately, eachimage affecting facimage because the result is affected motion estimation, registration, tor is extremely challenging. Simultaneously the image and In texture details andare remove three major contributions to improving the quality of the final reconstructed image, the influence of noise, we propose a new robust MFSR method.

Observation Model and Basic Framework of MFSR
Proposed MFSR Algorithm
Selecting the Appropriate LR Images and Alignment
Proposed Fidelity Term
Image Reconstruction
Experimental Results and Analysis
4.1.Experiments
We created
SSIM Results of our MFSR
Experiments on Real Data
Conclusions
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call