The L-1 norm regularized least squares method is often used for finding sparse approximate solutions and is widely used in signal restoration. Basis pursuit denoising (BPD) performs noise reduction in this way. However, the shortcoming of using L-1 norm regularization is the underestimation of the true solution. Recently, a class of non-convex penalties have been proposed to improve this situation. This kind of penalty function is non-convex itself, but preserves the convexity property of the whole cost function. This approach has been confirmed to offer good performance in 1-D signal denoising. This paper demonstrates the aforementioned method to 2-D signals (images) and applies it to multisensor image fusion. The problem is posed as an inverse one and a corresponding cost function is judiciously designed to include two data attachment terms. The whole cost function is proved to be convex upon suitably choosing the non-convex penalty, so that the cost function minimization can be tackled by convex optimization approaches, which comprise simple computations. The performance of the proposed method is benchmarked against a number of state-of-the-art image fusion techniques and superior performance is demonstrated both visually and in terms of various assessment measures. (c) 2020 Elsevier B.V. All rights reserved.

This paper presents a novel method for line restoration in speckle images. We address this as a sparse estimation problem using both convex and non-convex optimization techniques based on the Radon transform and sparsity regularization. This breaks into subproblems, which are solved using the alternating direction method of multipliers, thereby achieving line detection and deconvolution simultaneously. We include an additional deblurring step in the Radon domain via a total variation blind deconvolution to enhance line visualization and to improve line recognition. We evaluate our approach on a real clinical application: the identification of B-lines in lung ultrasound images. Thus, an automatic B-line identification method is proposed, using a simple local maxima technique in the Radon transform domain, associated with known clinical definitions of line artefacts. Using all initially detected lines as a starting point, our approach then differentiates between B-lines and other lines of no clinical significance, including Z-lines and A-lines. We evaluated our techniques using as ground truth lines identified visually by clinical experts. The proposed approach achieves the best B-line detection performance as measured by the F score when a non-convex l(p) regularization is employed for both line detection and deconvolution. The F scores as well as the receiver operating characteristic (ROC) curves show that the proposed approach outperforms the state-of-the-art methods with improvements in B-line detection performance of 54%, 40%, and 33% for F-0.5, F-1, and F-2, respectively, and of 24% based on ROC curve evaluations.

This paper presents a novel algorithm for terrain type classification based on monocular video captured from the viewpoint of human locomotion. A texture-based algorithm is developed to classify the path ahead into multiple groups that can be used to support terrain classification. Gait is taken into account in two ways. Firstly, for key frame selection, when regions with homogeneous texture characteristics are updated, the frequency variations of the textured surface are analyzed and used to adaptively define filter coefficients. Secondly, it is incorporated in the parameter estimation process where probabilities of path consistency are employed to improve terrain-type estimation. When tested with multiple classes that directly affect mobility-a hard surface, a soft surface, and an unwalkable area-our proposed method outperforms existing methods by up to 16%, and also provides improved robustness.

This paper presents a novel algorithm for terrain type classification based on monocular video captured from the viewpoint of human locomotion. A texture-based algorithm is developed to classify the path ahead into multiple groups that can be used to support terrain classification. Gait is taken into account in two ways. Firstly, for key frame selection, when regions with homogeneous texture characteristics are updated, the frequency variations of the textured surface are analyzed and used to adaptively define filter coefficients. Secondly, it is incorporated in the parameter estimation process where probabilities of path consistency are employed to improve terrain-type estimation. When tested with multiple classes that directly affect mobility-a hard surface, a soft surface, and an unwalkable area-our proposed method outperforms existing methods by up to 16%, and also provides improved robustness. =20

Colour volumetric data, which is constructed from a set of multi-view images, is capable of providing realistic immersive experience. However it is not widely applicable due to its manifold increase in bandwidth. This paper presents a novel framework to achieve scalable volumetric compression. Based on wavelet transformation, data rearrangement algorithm is proposed to compact volumetric data leading to high efficiency of transformation. The colour data is rearranged using the characteristics of human visual system. A pre-processing scheme for adaptive resolution is also proposed in this paper. The low resolution overcomes the limitation of the data transmission at low bitrates, whilst the fine resolution improves the quality of the synthesised images. Results show significant improvement of the compression performance over the traditional 3D coding. Finally, effect of using residual coding is investigated in order to show a trade off between the compression and view synthesis performance.

Systems with cheap/simple/power efficient encoders but complex decoders make applications such as low cost, low power remote sensors practical. Bandwidth considerations however are still an issue and compression efficiency has to remain high. In this paper, we present a distributed video codec (DVC) that we are developing with the aim of achieving such a low power paradigm at the cost of only a small compression performance deficit relative to the current state of the art, H.264. The proposed system employs spatial interleaving of KEY and Wyner-Ziv data which allows efficient side information (SI) generation through block-based error concealment, a Gray code that increases the accuracy of bit probability estimation, and a diversity scheme that produces more reliable results by exploiting multiple SI generated data. Simulation results show an improvement of the proposed scheme over H.264 intra coding of up to 1.5 dB. We additionally propose two mechanisms for selective parity bit feedback requests that can further reduce the WZ bitrate by up to 15%.

This paper presents a novel framework to achieve scalable multiview image compression and view synthesis. The open-loop wavelet-lifting scheme for geometric. filtering has been exploited to achieve signal-to-noise ratio scalability and view-type scalability ( mono, stereo, or multiview). Spatial scalability is achieved by employing in-band prediction which removes correlations among subbands (level-by-level) via shift-invariant references obtained by overcomplete discrete wavelet transforms. We propose a novel in-band disparity compensated view. filtering approach, akin to motion compensated temporal. filtering, for achieving a scalable multiview codec. In our codec, hybrid prediction is proposed to deal with occlusions, and a novel cost function in dynamic programming (DP) for disparity estimation is introduced to improve view synthesis quality. Experiments show comparable results at full resolution and significant improvements at coarser resolutions, compared to a conventional spatial prediction scheme. View synthesis efficiency is extensively improved by utilizing disparity estimation from the proposed DP approach.

This paper presents a novel framework to achieve scalable multiview image compression and view synthesis. The open-loop wavelet-lifting scheme for geometric. filtering has been exploited to achieve signal-to-noise ratio scalability and view-type scalability ( mono, stereo, or multiview). Spatial scalability is achieved by employing in-band prediction which removes correlations among subbands (level-by-level) via shift-invariant references obtained by overcomplete discrete wavelet transforms. We propose a novel in-band disparity compensated view. filtering approach, akin to motion compensated temporal. filtering, for achieving a scalable multiview codec. In our codec, hybrid prediction is proposed to deal with occlusions, and a novel cost function in dynamic programming (DP) for disparity estimation is introduced to improve view synthesis quality. Experiments show comparable results at full resolution and significant improvements at coarser resolutions, compared to a conventional spatial prediction scheme. View synthesis efficiency is extensively improved by utilizing disparity estimation from the proposed DP approach.

This paper presents a concealment based approach to distributed video coding that uses hybrid Key/WZ frames via an FMO type interleaving of macroblocks. Our motivation stems from a previous work of ours that showed promising results relative to the more common approach of splitting the sequence in key and WZ frames. In this paper, we extend our previous scheme to the case of I-B-P frame structures and transform domain DVC. We additionally introduce a number of enhancements at the decoder including use of spatio-temporal concealment for generating the side information on a MB basis, mode selection for switching between the two concealment approaches and for deciding how the correlation noise is estimated, local (MB wise) correlation noise estimation and modified B frame quantisation. The results presented indicate considerable improvement (up to 30%) compared to corresponding frame extrapolation and frame interpolation schemes.

Systems with cheap/simple/power efficient encoders but complex decoders make applications such as low cost, low power remote sensors practical. Bandwidth considerations however are still an issue and compression efficiency has to remain high. In this paper, we present a distributed video codec (DVC) that we are developing with the aim of achieving such a low power paradigm at the cost of only a small compression performance deficit relative to the current state of the art, H.264. The proposed system employs spatial interleaving of KEY and Wyner-Ziv data which allows efficient side information (SI) generation through block-based error concealment, a Gray code that increases the accuracy of bit probability estimation, and a diversity scheme that produces more reliable results by exploiting multiple SI generated data. Simulation results show an improvement of the proposed scheme over H.264 intra coding of up to 1.5 dB. We additionally propose two mechanisms for selective parity bit feedback requests that can fizrther reduce the WZ bitrate by up to 15%.