Due to substantial differences in appearance and movement between cartoon faces and real human faces, directly applying existing speech-driven speaker generation techniques to cartoon face images often fails to accurately capture key points, resulting in distorted expressions and animation artifacts. To address these challenges, this article proposes a speech-driven cartoon face animation generation model called CartoonTalk, which integrates 2D cartoon key points with 3D motion coefficients. The model comprises three modules: face annotation, motion extraction, and 3D facial rendering. The face annotation module first annotates a cartoon image, capturing its 2D key points. Subsequently, the motion extraction module derives 3D motion coefficients for head and facial expressions from speech data. Finally, the 3D facial rendering module uses these 2D and 3D features to generate the animated cartoon face. Quantitative and qualitative evaluations demonstrate that our model significantly outperforms existing approaches in both lip-sync accuracy and visual quality, establishing new state-of-the-art benchmarks on cartoon datasets.

Wildfire, one of the most serious disasters in the world, making a huge threat to local economic development and residents' life and property safety. Therefore, it is crucial to realize near real time wildfire detection in specific high wildfire risk area. Himawari-8 is a state of art Geostationary Orbit Satellites (GOS), which can realize near-real time wildfire detection and dynamic monitoring of wildfire changes. In this study, three features strategies: spectral, spectral calculated and spectral with spatial were used to extract features from Himawari-8 and Topography data. Next, Random Forest models were trained by these features. And then, we evaluated models on Himawari-8 data generated in March 29, 2020 6:10 UTC, March 30, 2020 5:50 UTC, March 31, 2020 5:30 UTC and April 1,2020 5:50 UTC to detect specific wildfire events in Southwestern China. The results showed an overall precision of 64.24%, 64.08% and 68.07%, and an overall F1-score of 0.737, 0.677, 0.724 based on three different features strategies. This study proved that our models have the ability to detect wildfire point accurately, and model with strategy.3 performed the best when considering precision, omission and F1-score.

To address the limitation of the high-dimensionality features and single spatial scale in the spectral–spatial classification of hyperspectral image (HSI), we propose a random multiscale convolutional network (RMCNet) that combines a multiscale dimensionality reduction module (MDRM) and the RMCNet for improving classification accuracy. The MDRM is based on multiscale superpixel segmentations, which implements dimensionality reduction leading to relieve the Hughes problem and reduce the computation burden in deep learning. Then, the multiscale spectral–spatial features are extracted by the RMCNet to adaptive various complex scenes in HSI. Finally, the multiscale spectral–spatial features act as inputs of support vector machine for classification. In the experiments, three benchmark HSIs are used to evaluate the performance of the proposed method. The experimental results demonstrate that the RMCNet can yield a competitive performance compared with the state-of-the-art methods.

Pan sharpening is used to fuse a low-resolution multispectral (MS) image and a high-resolution panchromatic (PAN) image to obtain a high-resolution MS image. This article proposes PSMD-Net, an end-to-end pan-sharpening method based on a multi-scale dense network. A shallow feature extraction layer (SFEL) extracts the shallow features from the original images, and these are used as an input to a global dense feature fusion (GDFF) network to learn the global features for image reconstruction. A multiscale dense block (MDB) is designed to fully extract the spatial and spectral information from the shallow features in the GDFF network. In the proposed network, multiple MDBs are stacked to extract rich, multi-scale dense hierarchical features, and a global dense connection (GDC) is designed to allow direct connections from the state of the current MDB to all subsequent MDBs to extract more advanced features. The extracted hierarchical features are sent to the global feature fusion layer (GFFL) to adaptively learn the global features for image reconstruction. Finally, global residual learning (GRL) is adopted to force the network to pay more attention to the changing part of the image. We perform experiments on simulated and real data from WorldView-2 and WorldView-3 satellites. Visual and quantitative assessment results demonstrate that PSMD-Net yields higher-resolution fusion images than the state-of-the-art methods.

Neonatal magnetic resonance (MR) images typically have low spatial resolution and insufficient tissue contrast. Interpolation methods are commonly used to upsample the images for the subsequent analysis. However, the resulting images are often blurry and susceptible to partial volume effects. In this paper, we propose a novel longitudinally guided super-resolution (SR) algorithm for neonatal images. This is motivated by the fact that anatomical structures evolve slowly and smoothly as the brain develops after birth. We propose a strategy involving longitudinal regularization, similar to bilateral filtering, in combination with low-rank and total variation constraints to solve the ill-posed inverse problem associated with image SR. Experimental results on neonatal MR images demonstrate that the proposed algorithm recovers clear structural details and outperforms state-of-the-art methods both qualitatively and quantitatively.

An improved dehazing algorithm based on dark channel theory is proposed, in order to solve the problems of colour distortion and halo effect which still exists in dark channel prior algorithm. The dark channel prior theory may lead to colour distortion in sky region. Firstly, the guided filter is used to refine the segmentation of the sky region, and the atmospheric light is estimated accurately. Then, the median filter is used to obtain the detailed edge information. So a more clear transmission can be gotten which effectively suppress the halo problem. Finally, the gamma correction is applied to enhance image lightness with an empirically selected gamma parameter. The experimental results show that the proposed algorithm can effectively remove the haze. It can correct the colour distortion of the sky area and eliminate the halo effect at the edge of the scene.

To solve the problem of low efficiency and colour distortion in several typical tone mapping operators for high dynamic range (HDR) images, the authors propose a novel tone mapping algorithm based on fast image decomposition and multi‐layer fusion. An input HDR image is firstly decomposed into a base layer and a detail layer by an improved guided filtering method. For the base layer, its dynamic range is compressed by the simulated camera response function. For the detail layer, it is enhanced to produce more fine structures and reduce halo effect by applying the guided image filter. The colour balance correction method is adopted to suppress colour distortion. The experiments on HDR images demonstrate that the proposed technique could bring better brightness, contrast, and visibility with less halo effect than other state‐of‐the‐art methods both qualitatively and quantitatively in most cases.

Sparse representation has recently attracted enormous interests in the field of image restoration. The conventional sparsity-based methods enforce sparse coding on small image patches with certain constraints. However, they neglected the characteristics of image structures both within the same scale and across the different scales for the image sparse representation. This drawback limits the modeling capability of sparsity-based super-resolution methods, especially for the recovery of the observed low-resolution images. In this paper, we propose a joint super-resolution framework of structure-modulated sparse representations to improve the performance of sparsity-based image super-resolution. The proposed algorithm formulates the constrained optimization problem for high-resolution image recovery. The multistep magnification scheme with the ridge regression is first used to exploit the multiscale redundancy for the initial estimation of the high-resolution image. Then, the gradient histogram preservation is incorporated as a regularization term in sparse modeling of the image super-resolution problem. Finally, the numerical solution is provided to solve the super-resolution problem of model parameter estimation and sparse representation. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed algorithm. Experimental results demonstrate that our proposed algorithm, which can recover more fine structures and details from an input low-resolution image, outperforms the state-of-the-art methods both subjectively and objectively in most cases.

Sparse dictionary learning has attracted enormous interest in image processing and data representation in recent years. To improve the performance of dictionary learning, we propose an efficient block-structured incoherent K-SVD algorithm for the sparse representation of signals. Without relying on any prior knowledge of the group structure for the input data, we develop a two-stage agglomerative hierarchical clustering method for block sparse representations. This clustering method adaptively identifies the underlying block structure of the dictionary under the restricted conditions of both a maximal block size and a minimal distance between the blocks. Furthermore, to meet the constraints of both the upper bound and the lower bound of the mutual coherence of dictionary atoms, we introduce a regularization term for the objective function to suppress the block coherence of the overcomplete dictionary. The experiments on synthetic data and real images demonstrate that the proposed algorithm has lower representation error, higher visual quality and better reconstructed results than other state-of-the-art methods.

To improve the performance of sparsity-based single image super-resolution (SR), we propose a joint SR framework of structure prior based sparse representation (SPSR). The proposed SPSR algorithm exploits the multi-scale spatial structural self-similarities, the gradient prior and nonlocally centralized sparse representation to formulate a constrained optimization problem for high-resolution image recovery. The high-resolution image is firstly initialized by exploiting cross-scale patch redundancy in an image pyramid from single input low-resolution image. Then the sparse modeling of the image SR problem is proposed to refine it further, where the gradient histogram preservation is incorporated as a regularization term. Finally, an iterative solution is provided to solve the problem of model parameter estimation and sparse representation. Experimental results on image super-resolution validate the generality, effectiveness and robustness of the proposed SPSR algorithm.

To solve the problem of low efficiency and poor effect of the current tone mapping methods for the high dynamic range images, the authors propose a hierarchical tone mapping algorithm based on colour appearance model. The discrete Gaussian kernel is used to speed up the bilateral filter. The operation of tone compression in RGB colour space is adopted to correct the colour casts. The extreme values of the pixels are also adjusted in the detail layer. Moreover, after the tone mapping, the colour saturation is enhanced in the image regions of rich details and sharp edges. Experimental results show that the proposed algorithm with less computational cost reduces the halo effect significantly, and achieves the natural colour and the rich details. It outperforms the state‐of‐the‐art methods in terms of visual quality and objective indicators.

The observed images are usually noisy due to data acquisition and transmission process. Therefore, image denoising is a necessary procedure prior to post-processing applications. The proposed algorithm exploits the self-similarity based low rank technique to approximate the real-world image in the multivariate analysis sense. It consists of two successive steps: adaptive dimensionality reduction of similar patch groups, and the collaborative filtering. For each target patch, the singular value decomposition (SVD) is used to factorize the similar patch group collected in a local search window by block-matching. Parallel analysis automatically selects the principal signal components by discarding the nonsignificant singular values. After the inverse SVD transform, the denoised image is reconstructed by the weighted averaging approach. Finally, the collaborative Wiener filtering is applied to further remove the noise. Experimental results show that the proposed algorithm surpasses the state-of-the-art methods in most cases.

There are various image filtering approaches in computer vision and image processing that are effective for some types of noise, but they invariably make certain assumptions about the properties of the signal and/or noise which lack the generality for diverse image noise reduction. This study describes a novel generalised guided image filtering method with the reference image generated by signal subspace projection (SSP) technique. It adopts refined parallel analysis with Monte Carlo simulations to select the dimensionality of signal subspace in the patch‐based noisy images. The noiseless image is reconstructed from the noisy image projected onto the significant eigenimages by component analysis. Training/test image are utilised to determine the relationship between the optimal parameter value and noise deviation that maximises the output peak signal‐to‐noise ratio (PSNR). The optimal parameters of the proposed algorithm can be automatically selected using noise deviation estimation based on the smallest singular value of the patch‐based image by singular value decomposition (SVD). Finally, we present a quantitative and qualitative comparison of the proposed algorithm with the traditional guided filter and other state‐of‐the‐art methods with respect to the choice of the image patch and neighbourhood window sizes.

In this paper, we report on a process-based approach to estimate leaf chlorophyll content from hyperspectral remote sensing imagery. Extensive field and laboratory measurements were conducted for ten sites in black spruce (Picea mariana) forests near Sudbury, Ontario, Canada in 2003 and 2004. Leaf optical spectra and chlorophyll content, leaf and canopy biophysical parameters, and forest background optical properties were collected. Hyperspectral remote sensing images were acquired by the Compact Airborne Spectrographic Imager (CASI) over the study sites within one week of ground measurements. Using measured data as inputs, a geometricaloptical model 4-Scale was investigated to estimate forest canopy reflectance. The simulated canopy reflectance agrees well with the CASI measured reflectance. A look-up-table approach was developed to provide the probabilities of viewing sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up-tables, leaf reflectance spectra were inverted from hyperspectral remote sensing imagery. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified leaf-level PROSPECT inversion model.