Comparative analysis of different wavelet filters for low contrast and brightness enhancement of multispectral remote sensing images

This paper presents wavelet filter based low contrast multispectral remote sensing image enhancement by using singular value decomposition (SVD). The input image is decomposed into the four frequency subbands through discrete wavelet transform (DWT), and estimates the singular value matrix of the low-low subband image and then, it reconstructs the enhanced image by applying inverse DWT. This technique is especially useful for enhancement of INSAT as well as LANDSAT satellite images for better feature extraction. The singular value matrix represents the intensity information of the given image, and any change on the singular values changes the intensity of the input image. The proposed technique converts the image into DWT-SVD domain and after normalizing the singular value matrix; the enhanced image is reconstructed with the help of IDWT. The visual and quantitative results clearly show the edge sharpness, increased efficiency and flexibility of the proposed method based on Meyer wavelet and SVD over the various wavelet filters and also with exiting GHE technique. The experimental results (Mean, Standard Deviation, MSE and PSNR) derived from Meyer wavelet and SVD show the superiority of the proposed method over conventional methods.