Super-Resolution using Combination of Wavelet Transform and Interpolation Based Method (original) (raw)
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Single Image Super Resolution using Interpolation and Discrete Wavelet Transform
International Journal of Trend in Scientific Research and Development
An interpolation-based method, such as bilinear, bicubic, or nearest neighbor interpolation, is regarded as a simple way to increase the spatial resolution for the LR image. It uses the interpolation kernel to predict the missing pixel values, which fails to approximate the underlying image structure and leads to some blurred edges. In this work a super resolution technique based on Sparse characteristics of wavelet transform. Hence, we proposed a wavelet based super-resolution technique, which will be of the category of interpolative methods, using sparse property of wavelets. It is based on sparse representation property of the wavelets. Simulation results prove that the proposed wavelet based interpolation method outperforms all other existing methods for single image super resolution. The proposed method has 7.7 dB improvement in PSNR compared with Adaptive sparse representation and self-learning ASR-SL [1] for test image Leaves, 12.92 dB improvement for test image Mountain Lion & 7.15 dB improvement for test image Hat compared with ASR-SL [1]. Similarly, 12% improvement in SSIM for test image Leaves compared with [1], 29% improvement in SSIM for test image Mountain Lion compared with [1] & 17% improvement in SSIM for test image Hat compared with [1].
Single image super resolution with improved wavelet interpolation and iterative back-projection
Spatial resolution of digital images is limited by practical considerations of digital imaging systems. Single image super resolution is therefore required to create images that allow better identification and interpretation of details. A number of investigations have been carried out on image super resolution using the discrete wavelet transform. In this paper, a comparative study is made of different interpolation based methods for the estimation of high frequency sub-bands for the super resolution image. An investigation of the effect of different parameters in bicubic interpolation kernel is also carried out. Based on the result, a new algorithm is proposed for single image super resolution using the discrete wavelet transform and incorporating iterative back-projection. The proposed method is tested against other approaches and found to give superior results in terms of peak signal to noise ratio and structural similarity index measure.
Image Super-resolution using Wavelet Transform and Bicubic Interpolation
This paper entitles the different method available for quality image super-resolution. Nowadays images are widely used over the internet and also in medical and security areas. Interpolation method is used for image superresolution but this method does not give you better results. This paper, proposes an image super-resolution technique based on Bicubic interpolation of high frequency subbands obtained from Discrete Wavelet Transform and the input image. Discrete Wavelet Transform is applied in order to decompose an input image into different subbands. The estimated high frequency subbands are being modified by using high frequency subbands obtained from Stationary Wavelet Transform. All these images are combined to generate new resolution enhanced image by using Inverse Discrete Wavelet Transform which gives you better quality output image. The proposed technique is tested on benchmark images. The quantitative (Peak signal-to-noise ratio) and visual results show the superiority of the proposed technique over conventional and state-of-art image enhancement technique. The PSNR improvement of the proposed technique is up to 5.04dB compared with standard bicubic interpolation
Redundant Wavelet Transform Based Image Super Resolution
2013
The process of Super Resolution (SR) aims at extracting a high resolution image from low resolution image. The proposed technique uses Redundant Wavelet Transform to enhance the resolution of an image using a single low resolution image. The proposed method decomposes the input image into different subbands. Then all subbands are interpolated. Combining all the interpolated subbands using Inverse Redundant Wavelet Transform provides the proposed super resolution image. The algorithm is tested with various wavelet types and their performance is compared. The proposed technique has been tested on Lena, Elaine, Pepper, and Baboon images. The proposed method gives higher quantitative peak signal-tonoise ratio (PSNR) and visual results in comparison to other conventional and state-of-art image Super resolution techniques. Index Term — Image Super Resolution, Interpolation, Discrete Wavelet Transform, Redundant Wavelet Transform.
Super-Resolution Using Edge Modification through Stationary Wavelet Transform
2014 18th International Conference on Information Visualisation, 2014
In this paper, a super-resolution technique is proposed that uses a combination of bicubic interpolation and wavelet transform. Bicubic interpolation produces a high resolution image but is prone to blurring artifact. So the blurring artifact is reduced in the wavelet domain. The input low-resolution is up-sampled using bicubic interpolation. The edges of the resultant highresolution image are enhanced using stationary wavelet transform (SWT). SWT is applied to the image to produce sub-bands of the image and then these sub-bands are modified by multiplying with a boost value. Then these sub-bands are combined using inverse stationary wavelet transform (ISWT) to produce the final highresolution image. The quantitative and qualitative analysis illustrate that the proposed technique is provides superior results as compared to other existing techniques.
WAVELET ANALYSIS BASED IMAGE SUPER RESOLUTION Prof
2017
The increase in demand and performance of personal computing digital image processing is widely being used in many applications. Digital image process has advantage in term of cost, speed and flexibility. The objective is to extract information from the scene is being viewed. Image resolution describes the amount of information contained by images. Resolution has been frequently referred as an important aspect of an image. Images are being processed in order to obtain more enhanced resolution. One of the commonly used techniques for image resolution enhancement is Interpolation. In this work, an image resolution enhancement technique has been proposed which generates sharper high resolution image. The proposed technique uses DWT to decompose a low resolution image into different subbands. Then the three high frequency sub-band images have been interpolated using bi-cubic interpolation. The high frequency sub-bands obtained by SWT of the input image are being incremented into the int...
An improved Wavelet Lifting based Image Super Resolution
Ever since over three decades, computers have been extensively used for processing and exhibiting images. The capability to process visual information from a super resolution image can improve the information present in the image. The inspiration is from a human eye which takes in raw images (noisy, blurred and translated) and constructs asuper resolution image. In this technique lifting wavelet transform and stationary wavelet transform is used to increase the spatial resolution .The wavelet domain filters support to model the regularity of natural images while the edge details of image get sharper while up sampling. An iterative back projection method is used to reconstruct the high resolution image in an efficient iterative manner. Index Terms-S uper resolution, lifting wavelet transform, stationary wavelet transform.
International Journal of Image Processing (IJIP), 2010
High Resolution images can be reconstructed from several blurred, noisy and aliased low resolution images using a computational process know as super resolution reconstruction. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. In this paper we concentrate on a special case of super resolution problem where the wrap is composed of pure translation and rotation, the blur is space invariant and the noise is additive white Gaussian noise. Super resolution reconstruction consists of registration, restoration and interpolation phases. Once the Low resolution image are registered with respect to a reference frame then wavelet based restoration is performed to remove the blur and noise from the images, finally the images are interpolated using adaptive interpolation. We are proposing an efficient wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the low resolution images are decomposed into many levels to obtain different frequency bands. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution we have proposed an adaptive interpolation technique. Our proposed wavelet based denoising with adaptive interpolation for super resolution reconstruction preserves the edges as well as smoothens the image without introducing artifacts. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution image with a high PSNR, ISNR ratio and a good visual quality.
ETRI Journal, 2010
In this paper, we propose a new super-resolution technique based on interpolation of the high-frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The proposed technique uses DWT to decompose an image into different subband images. Then the high-frequency subband images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new super-resolved image by using inverse DWT. The proposed technique has been tested on Lena, Elaine, Pepper, and Baboon. The quantitative peak signal-to-noise ratio (PSNR) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques. For Lena's image, the PSNR is 7.93 dB higher than the bicubic interpolation.
Wavelet-Based Interpolation With Least Squares for Resolution Image Enhancement
A new digital image interpolation method that is performed in the wavelet domain with a least squares algorithm is presented in this paper. This method estimates wavelet coefficients in the high frequency sub-images of the estimated High-Resolution (HR) image from the Low-Resolution (LR) image using a least squares algorithm. An inverse wavelet transform is then performed for the synthesis of the HR image. Experimental results show that the proposed method outperforms other commonly used methods such as the bilinear, bicubic, and traditional least squares methods, objectively and subjectively.