Compressive Sensing (CS) is an emerging new sampling technique which helps to break through the Nyquist sampling frequency for sparse signals. This paper addresses improving one of its recovery algorithms known as the Block Compressive Sensing with Smooth Projected Landweber (BCS-SPL). For reducing the blocking artifacts in BCS-SPL, the Wiener filter has been implemented as a classic way to smooth image at the beginning of each iteration, but it is quite sensitive to image edges and blurs the image. In this paper, we propose a modified method which separates image signal into its low and high frequency components, and then independently processes each of the two components. Subsequently, a smoothness enhancing operation is implemented to improve reduction of high frequency oscillatory artifacts after hard thresholding. Experimental results show that the proposed method improves reconstructed image quality by more than 3dB compared to the conventional BCS-SPL.
Although block compressive sensing (BCS) makes it tractable to sense large-sized images and video, its recovery performance has yet to be significantly improved because its recovered images or video usually suffer from blurred edges, loss of details, and high-frequency oscillatory artifacts, especially at a low subrate. This paper addresses these problems by designing a modified total variation technique that employs multi-block gradient processing, a denoised Lagrangian multiplier, and patch-based sparse representation. In the case of video, the proposed recovery method is able to exploit both spatial and temporal similarities. Simulation results confirm the improved performance of the proposed method for compressive sensing of images and video in terms of both objective and subjective qualities.
In compressive sensing (CS) of images, a block-based framework is preferred to avoid the huge memory and computation required for a frame-based approach. However, the recovered image suffers from blocking artefacts due to independent block processing, especially at a low subrate. As a result of this reported work the artifacts are reduced by weighted averaging adopting two techniques: overlapped CS recovery and adaptive weighting. Simulation results show its improvement in both subjective and objective qualities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.