Comparison of different algorithms to improve the quality of image
By: Sonavane, Mayur M
.
Contributor(s): Agrawal, S. S.
Publisher: Haryana IOSR - International Organization of Scientific Research 2023Edition: Vol.13(4), Jul-Aug.Description: 7-13p.Subject(s): EXTC EngineeringOnline resources: Click here
In:
IOSR journal of VLSI and signal processing (IOSR-JVSP)Summary: Super-resolution (SR) become the most important task in image processing because of requirement of
high quality of images. It becomes popular in technology like computer vision and pattern recognition. In Single
Image Super-resolution (SR) uses the sequences of single low resolution (LR) images from high resolution (HR)
to improve the quality of image. The SR is an image processing problem which aims to generate a high
resolution (HR) image from low resolution input image. The SR is difficult because of the missing information in
the given LR image. Thus in SR it is very important to boost the reconstruction performance so that, there is
need of effective prior knowledge. The prior knowledge of image will solve the problem to improve the quality of
generated image. The main aim of SR is to generate the HR image with good visual perception as similar as
original image. The paper compare two methods of SR that is bi-linear method, and direction group sparsity of
image method that is proposed one. These methods will evaluate the accuracy, subjective visual effect, quality of
image, time complexity and PSNR, etc. These calculations will enhance nature of picture like better visual
impact quality, lower reconstruction error and adequate calculation proficiency, and so on.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-1622 |
Super-resolution (SR) become the most important task in image processing because of requirement of
high quality of images. It becomes popular in technology like computer vision and pattern recognition. In Single
Image Super-resolution (SR) uses the sequences of single low resolution (LR) images from high resolution (HR)
to improve the quality of image. The SR is an image processing problem which aims to generate a high
resolution (HR) image from low resolution input image. The SR is difficult because of the missing information in
the given LR image. Thus in SR it is very important to boost the reconstruction performance so that, there is
need of effective prior knowledge. The prior knowledge of image will solve the problem to improve the quality of
generated image. The main aim of SR is to generate the HR image with good visual perception as similar as
original image. The paper compare two methods of SR that is bi-linear method, and direction group sparsity of
image method that is proposed one. These methods will evaluate the accuracy, subjective visual effect, quality of
image, time complexity and PSNR, etc. These calculations will enhance nature of picture like better visual
impact quality, lower reconstruction error and adequate calculation proficiency, and so on.
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