Image enhancement using fusion by wavelet transform and laplacian pyramid
This work addresses image quality improvement for applications like medical imaging or surveillance, but it is incremental as it builds on established fusion techniques.
The paper tackled image enhancement by fusing multiple image modalities using Laplacian pyramid and wavelet transform methods, resulting in decreased mean square error (MSE) and normalized absolute error (NAE) for Laplacian pyramid, with increased peak signal-to-noise ratio (PSNR) for both methods.
The idea of combining multiple image modalities to provide a single, enhanced image is well established different fusion methods have been proposed in literature. This paper is based on image fusion using laplacian pyramid and wavelet transform method. Images of same size are used for experimentation. Images used for the experimentation are standard images and averaging filter is used of equal weights in original images to burl. Performance of image fusion technique is measured by mean square error, normalized absolute error and peak signal to noise ratio. From the performance analysis it has been observed that MSE is decreased in case of both the methods where as PSNR increased, NAE decreased in case of laplacian pyramid where as constant for wavelet transform method.