16-09-2016, 10:22 AM
Satellite image resolution and brightness enhancement using discrete, stationary wavelet and singular value decomposition
1454770890-14.SatelliteimageresolutionandbrightnessenhancementusingDWTSWTandSVD.docx (Size: 38.34 KB / Downloads: 4)
Abstract--Satellite images are used in the field of research and video processing applications. One of the main issues of satellite image interpretation is its low resolution. Resolution is defined as the smallest number of discriminable detail in an image. There are two types of resolution, grey level resolution and spatial resolution. Grey level resolution enhances the smallest discriminable details in an image, i.e. we can discriminate change in grey level. Images are being processed in order to obtain more enhanced resolution. The proposed image resolution enhancement technique is based on high frequency sub-band images obtained by discrete wavelet transform (DWT) of the input image. First of all interpolate the input image using bicubic interpolation then perform DWT on the interpolated input image to obtain different sub-bands. The high frequency subband’s edges are enhanced using stationary wavelet (SWT).Combine these two high frequency subband’s which are modified and interpolated. Then perform inverse DWT (IDWT) to get high resolution image. To increase the brightness of an image, use SVD (singular value decomposition) and DWT. The quantitative and visual results are showing the superiority of the proposed technique
I. INTRODUCTION
An image is a rectangular grid of pixels. It has a definite height and a definite width counted in pixels. They may be captured by optical devices—such as cameras, mirrors, lenses, telescopes, microscopes, etc. and natural objects and phenomena, such as the human eye or water surfaces. The recent advances in low-cost imaging solutions and increasing storage capacities, there is an increased demand for good
image quality in a wide variety of applications involving both image and video processing [1].To acquire image data at a higher resolution ,which is technically not feasible. In some cases, it is the limitation of the sensor due to low-power requirements as in satellite imaging, remote sensing, and surveillance imaging. In other cases, it is the limitation of the sensed environment itself. One measurement of image quality is the resolution. Images are being processed to obtain the more enhanced resolution and improve the ability to discern important features in images
Interpolation in image processing is a well-known method to increase the resolution of digital image. It increases the number of pixels in a digital image. Interpolation method select new pixel from surrounding pixels. Interpolation has been widely used in many image processing applications such as facial reconstruction, multiple-description coding and resolution enhancement [2]. There are many interpolation technique has been developed to increase the quality of this task. Mainly there are three well-known interpolation techniques for image interpolation namely Linear, Nearest neighbor and Bicubic. Bicubic is more sophisticated than the other and produces smoother edges [2].
Image resolution enhancement in the wavelet domain is a relatively new research addition, and recently, many new algorithms and others have estimated at the unknown details of wavelet coefficients in an effort to improve the sharpness of reconstructed images [3].The 1-D DWT can be extended to 2-D transform using separable wavelet filters. With separable filters, applying a 1-D transform to all the rows of the input and then repeating on all of the columns can compute the 2-D transform. When one-level 2-D DWT is applied to an image, four transform coefficient sets are created. As depicted in Figure 1, the four sets are LL, HL, LH, and HH, where the first letter corresponds to applying either a low pass or high pass filter to the rows, and the second letter refers to the filter applied to the columns