483 results about "Upsampling" patented technology

The invention relates to the technical field of remote sensing image data processing, in particular to a super-resolution remote sensing image reconstruction method, system and device and a storage medium, and the method specifically comprises the steps: firstly extracting shallow layer features of a low-resolution remote sensing image, and processing the shallow layer features in two paths: branch 1: channel expansion reactivation, and outputting a first branch feature; and branch 2: after channel expansion, depth separable convolution reactivation processing is carried out, and second branch features are output through snakelike scanning. The two branches are fused into a snakelike feature, are connected with a shallow feature in a jumping manner, then are subjected to channel attention processing, and then are connected in a self-jumping manner to obtain a second connection feature. Grouping is carried out according to channels, multi-scale local features are generated through multiple times of neighborhood attention processing and splicing, and deep features are obtained after iteration. And finally connecting shallow and deep features in a jumping manner, and performing up-sampling to output a reconstructed image. According to the method, the high efficiency of snakelike visual state space processing and the excellent long-distance dependence modeling capability are fully exerted, and efficient and accurate sequence and space feature fusion is realized.

The invention provides a video snapshot compression imaging reconstruction method based on spatio-temporal deformable attention, which improves the reconstruction quality and efficiency, and comprises the following steps: inputting a single frame compression measurement value and a measurement matrix into an initial reconstruction module to obtain an initial reconstruction video frame; inputting the initial reconstructed video frame into a feature extraction encoder, mapping the initial reconstructed video frame to a high-dimensional feature space through multi-layer 3D convolution, and outputting a feature map; the feature map is input into a plurality of stacked DenseRNet Blocks, and the number of the DenseRNet Blocks is one; the DenseRNet Block internally comprises a plurality of DeT Blocks, after the DenseRNet Block dynamically divides an input feature channel, grouping progressive processing and feature fusion are carried out through the plurality of DeT Blocks, and the DeT Blocks comprise a deformable space convolution branch used for modeling local deformation perception, a time self-attention branch used for modeling global time sequence dependence and a feature interaction module used for cross-channel information interaction; and the features processed by the DenseRNet Block are input into a video reconstruction decoder, and a reconstructed video sequence is output through up-sampling of transposition convolution and refining of multilayer 3D convolution.

The invention discloses a pipeline defect magnetic flux leakage detection method and device based on multi-scale data driving deep learning, and relates to the technical field of pipeline defect detection. A multi-scale magnetic flux leakage signal data set containing defect global distribution and local details is generated, and multi-level primary features are automatically extracted by using a convolutional neural network; and the defects of poor generalization and easy key information omission of a manual method are overcome. And then dynamically enhancing and performing weighted fusion on multi-scale features by means of a multi-scale convolution branch and an attention mechanism, so that the model can learn global and local features of the defect at the same time, the problem that the global and local features of the defect are difficult to consider in traditional deep learning is solved, and through transverse connection and up-sampling fusion of a feature pyramid, a multi-scale feature is obtained. And the features have high semantic information and high spatial details. And finally, independently carrying out multi-task prediction by virtue of a task decoupling module, and calibrating result consistency by virtue of a task alignment module, so that the detection accuracy in a complex scene is improved, and more accurate and robust pipeline defect magnetic flux leakage detection is realized.

The invention provides a hybrid CNN-Transform colon polyp image segmentation method combining edge guidance and a double attention mechanism, and is applied to the technical field of medical data processing. According to the method, a CNN encoder is adopted to extract multi-scale local features, a Transform encoder is adopted to extract global context, an edge probability graph is generated by introducing an edge guide branch based on shallow layer features, a gating coefficient is calculated according to the edge probability graph and / or statistics obtained by intermediate prediction, weighted fusion is performed on the local and global features, and the local feature and the global feature are integrated. And the decoder performs up-sampling step by step and outputs a segmentation result. Compared with the prior art, the method has the advantages in boundary integrity and small target detection. Experiments show that on a Kvasair-SEG data set, the optimal Dice of a verification set of the scheme is 0.892, the optimal HD95 of the verification set of the scheme is 12.2, the Dice of a test set of the scheme is 0.89, and the optimal HD95 of the test set of the scheme of the scheme is 12.3.

The invention relates to a video snapshot compression imaging reconstruction method and system. The method comprises the following steps: inputting a video frame sequence and a time-varying mask set thereof into a measurement model to obtain initial estimation; constructing a reconstruction network which comprises a feature extraction module, a gating residual network module and a video reconstruction module; the feature extraction module comprises two three-dimensional convolution layers, each three-dimensional convolution layer is connected with an activation function, and the feature extraction module extracts initial features from the initial estimation; inputting the initial features into a gating residual network module, and outputting reconstruction information features; and the video reconstruction module fuses the reconstruction information features, and performs up-sampling and detail refining to reconstruct a video sequence. According to the method, on the premise that parameters and computing power are hardly increased, ghosting and flickering are effectively restrained, the stability of long-time reconstruction is improved, and an effective scheme is provided for SCI reconstruction with the high compression ratio, the super-definition resolution ratio and the long sequence.

The invention provides a voice synthesis method and system based on VITS improvement, and the method comprises the steps: optimizing a text encoder of a VITS model, introducing a large language model, and enabling the emotion, intention and speaking style of an input text to be captured when the text is encoded; a random disturbance item is introduced when the Q value is dynamically planned and solved, the alignment flexibility in the initial training stage is improved, meanwhile, monotonicity constraint is strictly kept, and it is avoided that a suboptimal solution is obtained through convergence too early; a ConvNeXt module is used as a basic backbone network of a decoder, and ISTFT is utilized to efficiently reconstruct a time domain signal, so that waveform up-sampling is realized, redundant calculation of traditional transpose convolution is avoided, and reasoning is accelerated. According to the method, the reasoning speed, the emotion expression ability and the style control flexibility of speech synthesis can be effectively improved, a new solution is provided for cross-language diversified speech synthesis, and a reference is provided for the more efficient and more intelligent development of the speech synthesis technology.

The invention discloses a feature hierarchical attention fusion and dynamic optimization method for single-stage target detection, and relates to the technical field of computer vision. The method comprises the steps of collecting image data, preprocessing an image and dividing the image into a training set, a test set and a verification set; the HAF-DRFPN neck network uses dynamic up-sampling single pixel point sampling to recover the feature resolution; feature extraction is optimized by using a gating residual mechanism and depth separable convolution; a feature refining feed-forward network is introduced through multi-layer perception generation weight, and feature expression is enhanced; respectively capturing and fusing superficial details and deep semantic information by utilizing channel and space attention and cross attention; a multi-branch dynamic sampling stacking framework is adopted, and cross-scale features are adaptively fused and optimized; multi-module cooperative processing is carried out on the whole architecture, and multi-scale features with higher discrimination are provided for the detection head; according to the method, the YOLO12 trunk and the detection head are connected with the HAF-DRFPN to form the model HAF-DRNet suitable for target detection, so that the flexibility and accuracy of single-stage target detection are improved.

The invention belongs to the technical field of radar signal processing, and particularly relates to a low-confidence small target detection method in radar echoes based on a hybrid architecture, and the method comprises the steps: firstly carrying out the spectrum symmetric movement and dimension recombination of radar echo data, and generating five-dimensional tensors [B, T, C, H, W] containing time sequence features; then the tensor is input into a detection model formed by cascading a Hurglass 3D module and a YOLOv8 network, the Hurglass 3D module extracts multi-scale spatial-temporal features through a structure of three-dimensional convolution down-sampling, bottleneck layer and three-dimensional transposition convolution up-sampling, and feature fusion is achieved through jump connection; and finally, target detection is completed through a backbone network, a neck network and a decoupling detection head of the YOLOv8 network. According to the invention, through spatio-temporal feature combined extraction and small target feature enhancement, the detection accuracy and the positioning precision of the low signal-to-noise ratio small target in radar echoes are effectively improved.

A data transmission method includes transmitting to-be-transmitted data in N frequency domain resource blocks, where each of the N frequency domain resource blocks includes K(n) subcarriers, where n=1, 2, . . . , N, N is greater than or equal to 1, and K(n) is greater than or equal to 1; performing inverse Fourier transform and an upsampling operation on the to-be-transmitted data in each of the N frequency domain resource blocks to form N groups of data sequences; and transmitting the N groups of data sequences.

The invention belongs to the technical field of image data processing, and particularly relates to a super-resolution remote sensing image reconstruction method, system and device based on frequency domain enhancement, and the method comprises the steps: S1, extracting the shallow features of a low-resolution remote sensing image; s2, inputting the shallow features into a plurality of cascaded frequencies for interactive processing, performing double-branch processing on the input features, performing inverse transformation after radial weighting on different frequency components in a frequency domain to obtain first features, and obtaining second features through depth separable convolution, an activation function, a selection scanning module and layer normalization; fusing the two branch features according to the weight, performing jump connection with the original features, performing enhancement through a feedforward network, performing repeated execution for a set number of times, and performing convolution and residual connection to obtain deep features; and S3, fusing the deep features and the shallow features, and outputting a super-resolution image through convolution and pixel rearrangement up-sampling. According to the method, texture details and edge contours of the remote sensing image can be more accurately reconstructed while the structural consistency is kept.

The application discloses a kind of deep learning methods for low-frequency SKA broadband effect and synthetic beam effect elimination, belong to radio astronomy image processing field, including steps: S1, establish FSAS, FERM and FGFN;S2, based on the FSAS, FERM and FGFN of S1 establishment establishes IFS-Transformer network model;S3, two FERM modules are applied to obtain dirty image I ∈ R H×W×C Low-level features F0 ∈ R H×W×C ;S4, F0 is input FGFN module, and coding part is completed after twice downsampling operation;S5, the feature obtained by coding part is passed through 3 FSAS and twice upsampling, realizes feature learning and decoding operation;S6, after being processed by two FERM, final recovered image is obtained.The effect elimination method based on deep learning can more effectively eliminate the joint of broadband effect and synthetic beam effect, and can greatly reduce the time of effect elimination to a greater extent to restore and reconstruct original sky brightness;The manual operation process of the method of the patent can be greatly simplified by using deep learning technology.

The invention discloses a lightweight detection method for small defect targets in a complex industrial scene, and relates to the technical field of target detection technology improvement and deep learning computer vision. Spatial shift operation and a layer-by-layer interaction mechanism are introduced into a feature channel through the SS-CMM module, cross-channel and cross-space effective information fusion is achieved, and therefore the relation between local texture details and the global context is enhanced, and the perception ability of fine defects under the complex background is remarkably improved; the C2PSA-Topk sparse attention mechanism is combined, dynamic selection and weighting of important features are achieved on channel and space double domains, redundant background interference is restrained, discriminative response of tiny defects is highlighted, and the recognition precision of the model for small-scale targets and boundary fuzzy defects is effectively improved. The MS-KSU module introduces a multi-scale convolution and adaptive interpolation strategy in an up-sampling stage, so that a global structure and fine-grained details can be considered in a feature recovery process, and the continuity and integrity of a defect region boundary are ensured.

The invention discloses an image fusion method based on multi-dimensional complementary features and multi-scale detail enhancement, and the method comprises the steps: calculating an edge image of a source image, and splicing the edge image with the source image in a channel dimension; enhancing feature textures and edge details by a high-frequency detail enhancement module; generating three feature maps with different scales step by step through continuous down-sampling operation; from the deepest scale, the infrared and visible light features of the hierarchy are fused through a parallel three-branch fusion module; the fused features are used for reconstructing a low-resolution fusion result, and the low-resolution fusion result is transmitted to an upper layer through up-sampling operation; and in the scale of the upper layer, the steps are repeated until a final full-resolution fusion image is generated. A parallel multi-dimensional complementary fusion network PMCFusion is set, cooperative perception and efficient integration of spatial features, channel correlation and frequency domain information are achieved through a parallel three-branch fusion module PTFM, and through cross-dimensional attention interaction, the PMCFusion can selectively enhance multi-dimensional features and achieve deep complementation.

A test and measurement device has an input port to receive a signal from a device under test (DUT), the signal having a symbol rate, one or more analog-to-digital converters (ADC) to convert the signal to waveform samples at a sampling rate, and one or more processors, when aliasing is present: up-sample a portion of the signal having aliased samples to produce up-sampled samples; use the up-sampled samples to produce a real-time waveform; perform clock recovery on the real-time waveform to produce a recovered clock; and resample the aliased samples to produce a non-aliased waveform.

The present disclosure relates to methods, apparatus, systems, and non-transitory computer-readable storage media for training and using a multi-scale machine learning model for the enhancement of compressed video. According to some examples, a computer-implemented method includes receiving a video at a content delivery service; performing an encode on a frame of the video by the content delivery service that converts the frame from a pixel domain to a transform domain and back to the pixel domain to generate first pixel values and a first residual for a block of the frame at a first resolution; generating a first set of features, by a machine learning model of the content delivery service, for an input, at a first resolution, of the first pixel values and the first residual of the block; generating a second set of features, by the machine learning model of the content delivery service, for an input, at a second lower resolution, of second pixel values and a second residual of the block; upsampling the second set of features to the first resolution to generate an upsampled second set of features; generating a modified version of the frame based on the first set of features and the upsampled second set of features; and transmitting the modified version of the frame to a frame buffer or from the content delivery service to a viewer device.

The invention discloses a frequency modulation and wavelet sub-band guided double-domain collaborative Transformer X-ray image denoising method, which comprises the following steps of: acquiring a noise-containing digital ray original image and a corresponding clear reference image, and constructing a data set after preprocessing the noise-containing digital ray original image and the corresponding clear reference image; constructing a network model of a double-domain collaborative coding-decoding architecture; performing 3 * 3 deep convolution on an input image to extract shallow layer features; in the encoding stage, ETB and AFMB are alternately stacked to represent local and global information, a WB-LKED module is embedded to strengthen fine-grained features, and WDB executes down-sampling and transmits high-frequency features to a decoding end; in the decoding stage, the WUB recovers the resolution through double-path up-sampling, integrates the same-scale features of an encoder, enhances details by using high-frequency features, splices the features, then carries out ETB and AFMB refining, obtains output features through 3 * 3 deep convolution, and combines a global residual error connection optimization result; and training the model by using the data set, inputting a to-be-denoised image, and outputting a final result. According to the method, the problems of weak complex noise interference resistance, poor detail retention effect and limited CNR improvement can be solved.

The invention discloses an improved YOLOv11-based steel rail defect detection algorithm and system, and aims to solve the problems of low manual inspection efficiency, high limitation of a traditional visual technology and difficulty in embedded deployment of a deep learning model. According to the scheme, YOLOv11n is used as a framework to carry out three core improvements: firstly, a C3k2-LSKA module is used to replace an original module to enhance the feature extraction capability; secondly, DySample dynamic up-sampling is adopted to replace traditional interpolation up-sampling, and small defect feature transfer is optimized; and finally, introducing windmill-shaped convolution PConv to realize sparse feature extraction. After training based on a constructed steel rail defect data set, a detection system based on PyQt5 is developed, and the detection system is successfully deployed in K230 embedded equipment integrating a high-definition camera and a touch display screen to form an acquisition-inference-display closed loop. Experiments show that the detection precision of the improved model is remarkably improved compared with that of the original YOLOv11, meanwhile, the lightweight characteristic is kept, high precision and high efficiency are effectively considered, and a reliable solution is provided for intelligent operation and maintenance of the railway track.

A deep-learning-based method for eliminating a broadband effect and a synthesized-beam effect in low-frequency Square Kilometre Array (SKA) is provided. The method includes: establishing a frequency-domain extraction module, a feature enhancement module, and a frequency-domain gating module; constructing, based on the frequency-domain extraction module, the feature enhancement module, and the frequency-domain gating module, a neural network model based on a frequency-domain self-attention mechanism; performing primary feature extraction on an input image based on the feature enhancement module to obtain a low-level feature; inputting the low-level feature into the frequency-domain gating module and performing downsampling to achieve feature encoding, so as to obtain an encoded feature; inputting the encoded feature into the frequency-domain extraction module and performing frequency-domain attention computation and upsampling to achieve feature decoding, so as to obtain a decoded feature; and inputting the decoded feature into the feature enhancement module to obtain a target restored image.

The invention belongs to the field of audio processing, and particularly discloses an audio depression detection method and system based on an improved convolutional neural network autoencoder, and the method comprises the steps: carrying out the potential feature extraction and compression of input original audio data, extracting the data into a low-dimensional and dense potential feature vector, and carrying out the recognition of the potential feature vector; and the information loss in the reconstruction process is reduced. According to the model part, a residual block structure is introduced into an encoder part, input low-layer information is reserved through jump connection, the learning ability of the network is enhanced, and the gradient disappearance problem is avoided. A transpose convolution operation is introduced into a decoder part, the spatial resolution is increased through up-sampling, and a high-quality signal reconstruction task is taken as a constraint, so that the extracted potential features are ensured to contain all key information for accurate classification. Finally, the potential features are directly used for depression classification, and experimental results show that the accuracy and robustness of depression detection can be remarkably improved, and the method has high practical application value.

The invention discloses a sound source localization and distance measurement method, device and equipment based on a microphone array and a storage medium, and relates to the technical field of acoustics, and the method comprises the steps: collecting a frequency sweeping signal played by a sound source through the microphone array, obtaining the audio data of each channel, obtaining a cross-correlation sequence through generalized cross-correlation and phase transformation weighting processing, and obtaining a distance measurement result; constructing a current to-be-processed set, processing other sequences again by taking the first sequence as a reference to obtain a new sequence group, discarding the reference sequence, taking the new group as a to-be-processed set, repeating the iteration step until only one to-be-processed sequence is left in the set, and performing up-sampling on the sequences to obtain a to-be-processed set; extracting a first main peak position to obtain a time delay estimation value of a decimal sampling level so as to determine an incident angle of a sound source relative to an array normal, performing time delay alignment on each cross-correlation sequence, constructing a single-channel beam forming signal based on an alignment result, and determining a second main peak position so as to determine a sound source distance, the efficiency of sound source localization and distance measurement is improved.

The invention discloses a frame generation method and system adopting predictive sparse rendering, and belongs to the technical field of computer graphics, and the method comprises the steps: obtaining historical frame rendering data, current frame geometric rendering data and current frame sparse rendering data in a graphic rendering pipeline, and extracting a multi-scale feature pyramid through different encoders; receiving and fusing the multi-scale feature pyramids through a unified decoder, and generating a frame synthesis residual error, an extrapolation error score and a time sequence mixed weight through multi-stage up-sampling; performing time sequence synthesis by using the time sequence mixed weight and fusing the frame synthesis residual error to obtain a final rendering result of the current frame, and updating the final rendering result into historical frame rendering data of the next frame; and obtaining a next-frame sparse rendering mask by using the extrapolation error score and optical flow prediction, and generating next-frame sparse rendering data from the next-frame sparse rendering mask through tile-level sparse rendering. According to the method, the frame generation quality and the time stability can be remarkably improved under the condition of low rendering budget.

The invention discloses an alluvial island instantaneous waterline segmentation method fusing MobileViT and dynamic resampling. The method comprises the steps that a remote sensing image, shot by an unmanned aerial vehicle, of an alluvial island area is acquired and preprocessed; and inputting the preprocessed remote sensing image into the trained semantic segmentation model for processing, and accurately and efficiently obtaining a segmentation result of the instantaneous waterline in the remote sensing image. The semantic segmentation model is a lightweight ALISEg model constructed based on a DeepLabv3 + framework, a MobileViT structure is introduced into an encoder part, local modeling advantages and global perception capability are integrated, so that local detail recognition capability and global context perception capability are greatly enhanced, a DSC-ASPP module is designed, depth separable cavity convolution and a CBAM attention mechanism are combined, and the semantic segmentation model has the advantages of being high in robustness, high in robustness and high in robustness. And multi-scale spatial features can be fully captured. And meanwhile, a DySample module and an H-RAMi module are introduced into a decoder part, so that the up-sampling precision and the multi-scale feature fusion effect are improved, and the details of the water edge line are better reduced.

In one implementation, a method of decoding point cloud data for a point cloud is presented. A signal indicative of a number of sub-blocks, N1 is decoded, and a neural network is configured to have N1 sub-blocks. In particular, each sub-block of the N1 sub-blocks includes an upsampling function and at least a neural network layer, and each of the N1 sub-blocks is configured with the same neural network parameters. The point cloud data is decoded based on the neural network. At the encoder side, the signal indicative of N1 is encoded, and the neural network is configured to have N1 sub-blocks. In particular, each sub-block of the N1 sub-blocks includes a downsampling function and at least a neural network layer, and each of the N1 sub-blocks is configured with the same neural network parameters. The point cloud data is encoded based on the neural network.

Certain aspects of the present disclosure provide techniques for upsampling input data including inputting input data at a first resolution into a machine learning (ML) model comprising a plurality of selectivity kernels, each of the plurality of selectivity kernels configured to perform a different type of selectivity to upsample the input data; and obtaining output data, corresponding to the input data, at a second resolution, from the ML model, the second resolution being higher than the first resolution, wherein the output data is based on a composite of outputs from the plurality of selectivity kernels.

A mechanism for processing video data is disclosed. The mechanism includes determining a neural-network post-filter (NNPF) purpose based on a neural-network post-filter characteristics (NNPFC) supplemental enhancement information (SEI) message. A conversion is performed between a visual media data and a bitstream based on the NNPF purpose. Multiple input pictures are used for the NNPF purpose, and the NNPF is enabled to selectively generate output pictures for some input picture(s) and not to generate output pictures for other input picture(s).

The invention discloses a ring stationary signal extraction method based on a multi-scale gating attention mechanism and BiLSTM, and belongs to the technical field of mechanical fault diagnosis and signal processing. The objective of the invention is to solve the problem that weak fault features are difficult to accurately extract in a strong background noise and non-Gaussian abnormal interference environment in the prior art. Comprising the steps that a deep neural network model is constructed, local waveforms of different receptive fields are captured through parallel convolution branches, channel masks are generated through an attention mechanism, and hierarchical adaptive suppression of background noise is achieved; biLSTM is embedded in a bottleneck layer, a bidirectional memory unit of the BiLSTM is used for carrying out complete sequence time sequence modeling on compression features, and random abnormal pulses which do not conform to periodic logic are accurately eliminated through a long-term evolution rule of the sequence features; and finally, a decoder is adopted to carry out nonlinear shaping and detail repairing on the up-sampling features after transposed convolution. Gaussian white noise and non-Gaussian outliers can be efficiently filtered out, and pure ring stationary fault impact signals are reconstructed in a high-fidelity mode.

The invention discloses a dual-frequency ground penetrating radar underground target saliency feature fusion method, which comprises the following steps of: before target feature detection, removing a direct wave strong interference signal in GPR echo data; designing a semantic feature extraction model, fusing a channel attention SE module and a space attention PSANet module into a feature extraction network, and adding phase consistency loss and phase change region gradient loss into a loss function during network model training; transpose convolution is carried out on the extracted low-frequency-band semantic feature map in the double-frequency GPR data, upsampling is carried out to the scale of the high-frequency-band semantic feature map, and high-frequency-band and low-frequency-band feature fusion is carried out. According to the fusion method, the high-frequency-band GPR echo signal contains target information with higher resolution, the low-frequency-band GPR can detect a target located at a deeper position underground, the contradiction problem between the GPR detection depth and the detection precision is effectively solved, and the method has the advantages of being high in precision and robustness.

A feature extraction method is provided. The feature extraction method includes applying a first feature extracted from multi-channel input data to a bottleneck-based block included in a first type of path of a neural network and obtaining a second feature including a reduced parameter compared to the first feature, upsampling a derived feature of the second feature obtained based on a layer included in a second type of path of the neural network to correspond to a size of a derived feature of the first feature, and obtaining an intermediate feature applied to a head for a task of the neural network, based on the upsampled derived feature of the second feature and the derived feature of the first feature.

The invention provides a bridge vibration displacement detection method and system based on image enhancement and space-time tracking, and relates to the technical field of bridge detection and computer vision, and the method comprises the steps: obtaining a video image frame sequence of a bridge structure, carrying out the cooperative processing of image frames through employing an ACAE adaptive image enhancement algorithm, and obtaining an enhanced reconstructed image; inputting the reconstructed image into an AHSD-Net detection model, introducing a depth separable convolution module into a backbone network of the AHSD-Net detection model, performing spatial aggregation of preliminary features in a neck network by combining a dynamic upsampling mechanism, and finally outputting to obtain a target position detection result; based on a cascaded ST-Refine Track strategy, density denoising and position correction of a space dimension, cross-frame association of a time sequence dimension and adaptive smoothing processing of a state dimension are sequentially executed on a target position detection result, and a continuous and stable bridge vibration displacement curve is obtained. According to the invention, the displacement measurement precision and reliability are improved.

A method of processing video data performed by a decoder includes: decoding a bitstream to obtain video data and coding information, the coding information comprising weighting map indication information for defining a weighting map and filter coefficients optimized for the weighting map; obtaining a picture block based on the video data; upsampling the picture block; determining the weighting map using the weighting map indication information; and obtaining an enhanced picture block by applying a signal enhancement filter using the filter coefficients, together with the weighting map, to the upsampled picture block.