Comparison of DWT, DWT-SWT, and DT-CWT for low resolution satellite images enhancement

Abstract

In this research letter, a low resolution (LR) satellite image is enhanced to high resolution (HR) image using various wavelet techniques. One of the key factors is image resolution. Image RE is the method of modifying a portrait so that output portrait is more superior to the novel one for meticulous application. There are many techniques for portrait enhancement in image processing. Mainly three important wavelet techniques are used i.e., Method1 is Discrete wavelet transform (DWT), Method2 is Discrete wavelet transform — Stationary wavelet transforms (DWT-SWT), and Method3 is Dual tree complex wavelet transform (DT-CWT). A low resolution portrait is decomposed into four sub-band (SB's) i.e., Low-Low (SB 1), low-high (SB 2), high-low (SB 3), and high-high (SB 4) using DWT, DWT-SWT, and DT-CWT. The high frequency (HF) SB's are interpolated using bicubic interpolation and combined with interpolated low resolution portrait in case of DWT-SWT and DT-CWT. SB 1, SB 2, SB 3, SB 4 are interpolated using bicubic interpolation and combined with difference of input portrait and interpolated Low frequency SB portrait in case of DWT. Inverse DWT, Inverse DWT-SWT, and Inverse DT-CWT wavelet transform are applied respectively. The output is high resolution (HR) portrait. Image Resolution enhancement loses HF components. So DWT technique is used to preserve HF components. DWT generates artifacts. These artifacts are reduced by DT-CWT. All these three techniques are applied on NOAA-19-HRPT satellite images. The quantitative PSNR, RMSE, and CC are calculated for satellite portraits and compared for these three wavelet techniques.