Multiscale Adaptive Side Window Filtering and Its Application on Seismic Data

Abstract

Side window filtering (SWF) can effectively capture detailed image edges and is widely applied in image processing. However, its fixed-scale (or fixed-size) filter kernel cannot adapt to complex images, and the final output at the target pixel is only determined by the side window output with the minimum error functional, limiting its filtering capability. To further enhance the filtering capability of SWF, we first extend the traditional side windows with fixed-scale filter kernel to multiscale side windows by introducing the multiscale filter kernels, which leads to better complex image matching. Then, we further introduce an adaptively weighted parameter, which is inversely proportional to the error functional, to fully consider the contributions of all multiscale side windows to the final output. We finally propose the multiscale adaptive SWF (MASWF). Synthetic and field seismic data examples demonstrate that MASWF is a good potential technique for seismic data random noise attenuation and can be widely used in digital signal processing fields.