52 results about "Inverse discrete wavelet transform" patented technology

A method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of wavelet de-noising a data set comprised of a new set of frames of wavelet coefficients partially generated through a cyclic shift algorithm. The method preferably operates on a data set having 2^N frames, and the iteration is performed N−1 times. The resultant wavelet coefficients are then linearly averaged and an inverse discrete wavelet transform is performed to arrive at the de-noised original signal. The method is preferably carried out in a digital processor.

A signal processing method and system combines multi-scale decomposition, such as wavelet, pre-processing together with a compression technique, such as an auto-associative artificial neural network, operating in the multi-scale decomposition domain for signal denoising and extraction. All compressions are performed in the decomposed domain. A reverse decomposition such as an inverse discrete wavelet transform is performed on the combined outputs from all the compression modules to recover a clean signal back in the time domain. A low-cost, non-drug, non-invasive, on-demand therapy braincap system and method are pharmaceutically non-intrusive to the body for the purpose of disease diagnosis, treatment therapy, and direct mind control of external devices and systems. It is based on recognizing abnormal brainwave signatures and intervenes at the earliest moment, using magnetic and/or electric stimulations to reset the brainwaves back to normality. The feedback system is self-regulatory and the treatment stops when the brainwaves return to normal.

This invention has as its object to attain efficient encoding/decoding even when the processing time, memory, arithmetic cost, and the like of an apparatus are limited.

Encoded image data is input to an image processing apparatus (1) to obtain transform coefficients of subbands. The transform coefficients required to decode an image from its head position line by line are encoded by Golomb coding using one line of a subband as a unit to generate a code sequence, which is output to an image output apparatus (2). The image output apparatus (2) reconstructs an image by decoding the Golomb encoded data, dequantizing the coefficients, and computing the inverse discrete wavelet transforms of the coefficients, and outputs the reconstructed image.

A signal processing method and system combines multi-scale decomposition, such as wavelet, pre-processing together with a compression technique, such as an auto-associative artificial neural network, operating in the multi-scale decomposition domain for signal denoising and extraction. All compressions are performed in the decomposed domain. A reverse decomposition such as an inverse discrete wavelet transform is performed on the combined outputs from all the compression modules to recover a clean signal back in the time domain. A low-cost, non-drug, non-invasive, on-demand therapy braincap system and method are pharmaceutically non-intrusive to the body for the purpose of disease diagnosis, treatment therapy, and direct mind control of external devices and systems. It is based on recognizing abnormal brainwave signatures and intervenes at the earliest moment, using magnetic and/or electric stimulations to reset the brainwaves back to normality. The feedback system is self-regulatory and the treatment stops when the brainwaves return to normal. The braincap contains multiple sensing electrodes and microcoils; the microcoils are pairs of crossed microcoils or 3-axis triple crossed microcoils.

A signal processing method and system combines multi-scale decomposition, such as wavelet, pre-processing together with a compression technique, such as an auto-associative artificial neural network, operating in the multi-scale decomposition domain for signal denoising and extraction. All compressions are performed in the decomposed domain. A reverse decomposition such as an inverse discrete wavelet transform is performed on the combined outputs from all the compression modules to recover a clean signal back in the time domain. A low-cost, non-drug, non-invasive, on-demand therapy braincap system and method are pharmaceutically non-intrusive to the body for the purpose of disease diagnosis, treatment therapy, and direct mind control of external devices and systems. It is based on recognizing abnormal brainwave signatures and intervenes at the earliest moment, using magnetic and/or electric stimulations to reset the brainwaves back to normality. The feedback system is self-regulatory and the treatment stops when the brainwaves return to normal. The braincap contains multiple sensing electrodes and microcoils; the microcoils are pairs of crossed microcoils or 3-axis triple crossed microcoils.

An object of this invention is to efficiently attain noise removal. In order to achieve the object, the discrete wavelet transforms of an input image are computed to output wavelet coefficients of respective subbands (S301). Appropriate threshold values are respectively set for subbands HL, LH, and HH indicating high-frequency components (S302a, S302b, S302c). The wavelet coefficients of the subbands HL, LH, and HH then undergo threshold value processes using the set threshold values for the respective subbands (S303a, S303b, S303c). Pixels to be processed in a coefficient conversion process are determined based on the threshold value processing results of the respective subbands (S304). The wavelet coefficients of the respective subbands corresponding to the pixels to be processed determined in step S304 undergo coefficient conversion (S305), and the converted transformation coefficients undergo inverse discrete wavelet transformation, thus reconstructing and outputting a noise-removed image (S306).

In order to eliminate noise from an X-ray image by obtaining transform coefficients of a wavelet transform based upon information contained in tile-by-tile image data and obtaining image data based upon these transform coefficients, the entirety of a pre-processed original image is segmented into a plurality of tiles (S301). Wavelet transform coefficients of each tile obtained by segmentation are output (S302). Each tile is subjected to texture analysis and the results of analysis are output (S303). Next, a coefficient conversion is applied to the wavelet transform coefficients of each tile based upon the results of analysis (S304). High-frequency components among the transform coefficients are subjected to coefficient conversion. Next, an inverse discrete wavelet transform is applied to the wavelet transform coefficients of each area that has undergone conversion, whereby an image from which noise has been eliminated is output (S306).

An improved noise reduction process by wavelet thresholding utilizes a discrete wavelet transform to decompose the image into different resolution levels. A thresholding function is then applied in different resolution levels with different threshold values to eliminate insignificant wavelet coefficients which mainly correspond to the noise in the original image. Finally, an inverse discrete wavelet transform is applied to generate the noise-reduced video image. The threshold values are based on the relationships between the noise standard deviations of different decomposition levels in the wavelet domain and the noise standard deviation of the original image.

A hybrid distributed video encoding method based on wavelet domain intra-frame mode decision capable of improving rate aberration performance, is characterized by the following steps: (1) a low complexity encoding, including the following steps: using traditional intra-frame encoder to code key frames, generating the reference frame of Wyner-Ziv frame by weighted average interpolation, generating a residual frame by a subtraction arithmetic, performing discrete wavelet switch DWT to the residual frame, generating a wavelet block, intra-frame mode decision of the wavelet block, entropy coding of the mode information, and inter-frame SW-SPIHT coding or intra-frame SPIHT coding of the wavelet block; (2) a high-complexity decoding, including the following: using traditional intra-frame decoding algorithm to decode key frames, adopting a motion estimation interpolation to produce side information frame of the Wyner-Ziv, generating the reference frame of the decoding terminal by weighting average interpolation, generating a residual frame of the decoding terminal by a subtraction arithmetic, performing DWT to the residual frame, entropy decoding of the mode information, adopting LBS to perform motion estimation to generate more accurate side information, fine reconstruction of wavelet coefficients, and recovering the original pixels by inverse discrete wavelet transform (IDWT) and addition operations.

The invention provides a text image watermark embedding method and system, and a text image watermark extraction method and system, and the text image watermark embedding method comprises the steps: carrying out the secondary encryption of an input watermark, and obtaining a watermark to be embedded through error correction coding; performing secondary discrete wavelet transform on the text imageto obtain four first frequency band coefficient matrixes; performing block discrete cosine transform on the first frequency band coefficient matrix to obtain a corresponding first block coefficient matrix; for each first block coefficient matrix, selecting an intermediate frequency coefficient, and embedding a watermark by modifying the coefficient to obtain each first block coefficient matrix embedded with the watermark; and performing inverse discrete cosine transform and inverse discrete wavelet transform on each first block coefficient matrix embedded with the watermark to obtain a text image embedded with the watermark. According to the embodiment of the invention, on the premise that the invisibility of the watermark is ensured, the robustness of the watermark for resisting attacks such as compression, noise and shearing and the algorithm security are improved.

The invention discloses a dual encryption based DWT-DCT domain audio public watermarking algorithm. The algorithm comprises a watermark embedding process and a watermark detection process. The watermark embedding process sequentially comprises dual encryption of a binary watermark image, segment treatment on an original voice frequency, DWT and DCT of each segment coefficient, quantization embedding, inversed discrete cosine transform (IDCT) and inversed discrete wavelet transform (IDWT) of a quantization coefficient, reorganization and obtaining of a voice frequency containing watermark information. The watermark detection process sequentially comprises segment treatment on an audio signal to be tested, DWT and DCT of each segment coefficient, summing of watermark reference values at corresponding positions of n watermark coefficient series, reconfiguration of a two-dimensional image, inverted scrambling treatment and obtaining of a restored watermark image. By the aid of the algorithm, the transparency of watermarks is effectively guaranteed, the miss ratio of watermarks is greatly reduced, and the robustness and the accuracy of the watermark detection are improved.

The invention discloses a method for learning super-resolution network based on sub-band of group structure in wavelet domain, which realizes super-resolution restoration of single picture by constructing a novel super-resolution network comprising a feature extraction network and an image restoration network. The input low-resolution image is represented as a set of feature maps by a feature extraction network. The image restoration network includes a cluster upsampling module and a convolution layer. The output features of the feature extraction network are transferred to the cluster upsampling module for sub-band extraction, self-residual learning and sub-band refinement. The inverse discrete wavelet transform is used to change the resolution of the feature image so as to recover the feature image with high resolution. Then the high resolution image is restored by convolution layer. The method of the invention has good quantization index and can effectively realize super-resolutionrestoration of a single picture.

The invention discloses a wavelet information redundancy based infrared image denoising algorithm, which is implemented through acquiring multiple sets of wavelet coefficients containing similar redundant information in a discrete wavelet transform (DWT) process respectively in different downsampling modes; carrying out nonlinear transformation on high-frequency wavelet coefficients by using noise estimation parameters as the basis of a nonlinear transformation function threshold so as to suppress high-frequency noises; then reconstituting multiple images by using converted inverse discrete wavelet transform (IDWT); and carrying out weighting by using the multiple images containing similar redundant information so as to further remove the high-frequency noises, thereby obtaining high-quality infrared images. In case that infrared image denoising processing is performed by the algorithm disclosed by the invention, the detail information of original image can be kept, and high-frequency noise of images can be removed by filtration significantly, thereby improving the image resolution; and the algorithm can be implemented by using a monolithic FPGA (Field Programmable Gate Array), thereby facilitating the implementation of miniaturization, low cost and low power consumption.

The invention provides a complete blind detection robust quantization watermarking method. The present robust quantization watermarking method is not capable of realizing complete blind detection. To solve the problem, the invention provides a complete blind detection robust quantization watermarking method introduced into self-embedding thought. The method comprises the following steps of: performing discrete wavelet transform to a primary image at an embedding end, dividing a wavelet low-frequency sub-belt into sub-blocks which are not overlapped with each other, performing discrete cosine transform to each sub-block, generating a characteristic watermark by judging highest bit number parity of a direct current coefficient of each sub-block, then embedding the characteristic watermark into each sub-block of the wavelet detail sub-belt by using quantization regulation, and finally obtaining images containing the watermark by performing inverted discrete wavelet transform. Characteristic watermark extraction at a detection end is similar to characteristic watermark generation at the embedding end; and the watermark is identified for realizing blind detection. By the method of the invention, the complete blind detection is achieved by combining the self-embedded characteristic watermark and the blind extraction test watermark. The method has strong robustness in noise adding resistance, cutting, re-sampling, smooth and geometry attacks such as randomly canceling lines, randomly canceling rows and shifting lines rightwards.

The invention discloses a multilevel IDWT (Inverse Discrete Wavelet Transform) parallel processing method and system. The multilevel IDWT parallel processing system comprises a wavelet coefficient memory, a wavelet coefficient distributor, at least one wavelet coefficient line buffer and at least one IDWT calculation engine. The method comprises the following steps that the wavelet coefficient distributor extracts a wavelet coefficient of each wavelet decomposition level from the wavelet coefficient memory according to a wavelet coefficient distribution strategy and stores into the corresponding wavelet coefficient line buffer; the IDWT calculation engine reads the wavelet coefficient lines from the wavelet coefficient line buffer in sequence according to an order provided by the wavelet coefficient distribution strategy in order to carry out IDWT processing, and the multilevel IDWT parallel processing is realized through at least one IDWT calculation engine. According to the multilevel IDWT parallel processing method and system, the memory capacity and the memory bandwidth are reduced, the storage resource is saved and the time delay is greatly reduced; and furthermore, the method and the system have the advantages of good expansibility and high efficiency.

The invention relates to an image splicing method based on discrete wavelet transform and a gradient fusion algorithm. Firstly, projection transformation is performed on a reference image and a targetimage; feature points are extracted through an SURF algorithm, and registration is carried out through a KNN algorithm and an RANSAC algorithm; and then searching an optimal suture line to determinea fusion area and a mask image thereof, carrying out discrete wavelet transform, then respectively fusing the decomposed low-frequency area and the decomposed high-frequency area by using a gradient fusion algorithm, generating a fusion image by using inverse discrete wavelet transform, and finally splicing by using the mask image to obtain a final spliced image. According to the invention, the image splicing quality can be improved.

The invention relates to a denoising method and denoising devices for a dual tone multi frequency signal. The method comprises steps of performing discrete wavelet transform on an original dual tone multi frequency signal so as to obtain a transformation signal; filtering the transformation signal according to a preset filtration condition; and performing inverse discrete wavelet transform on the filtered transformation signal so as to obtain a denoised dual tone multi frequency signal. The disclosed terminal can firstly perform discrete wavelet transform on the original dual tone multi frequency signal so as to obtain the transformation signal, then, the transformation signal is filtered according to the preset filtration condition, and inverse discrete wavelet transform is performed on the filtered transformation signal, so that the noise contained in the original dual tone multi frequency signal is eliminated, and the recognition efficiency and recognition accuracy of the dual tone multi frequency signal are improved.

A traffic information providing system wherein even in any different traffic environment, traffic information expressed in any degree of detail can be compressed to a data amount in accordance with such a different traffic environment. The system includes a traffic information providing device (30) that comprises means (33) for producing sampling data from traffic information expressed by a function of distance from a reference position on a load and by a function of time, and means (34) for subjecting the sampling data to a discrete wavelet transformation to transform the traffic information to a scaling coefficient and a wavelet coefficient. The system also includes a traffic information utilizing device (60) that subjects the scaling coefficient and wavelet coefficient received from the traffic information providing device (30) to an inverse discrete wavelet transformation to reconstruct the traffic information. Even when the traffic information providing device (30) provides the scaling coefficient and wavelet coefficient without awareness of the traffic environment and reception status, the receiving side can reconstruct general and detailed information within a range of the received information.

The object of the present invention to provide a structure and method of processing signals, which includes transforming a signal into subbands by applying a forward discrete wavelet transform (FDWT) to the signal, partitioning the subbands into overlapping subband subsets, inverse transforming the overlapping subband subsets into signal subsets by applying at least one inverse discrete wavelet transform (IDWT) to the overlapping subband subsets, and combining the signal subsets to reproduce the signal. The IDWT is applied to all of the subband subsets in parallel.

The invention discloses a wavelet denoising method for partial discharge of DC gas-insulated electrical equipment. The wavelet denoising method comprises the following steps of: (1) acquiring partialdischarge current pulse signals of a typical insulation defect of the DC gas-insulated electrical equipment; (2) selecting an optimal wavelet by using a dynamic time warping method, determining an optimal signal decomposition layer number, carrying out decomposition processing on the partial discharge current pulse signal through discrete wavelet transform, and extracting a wavelet high-frequencycomponent of each layer and a wavelet low-frequency component of the highest layer; (3) processing the wavelet high-frequency components of each layer by using automatic threshold value and intermediate threshold value functions, removing noise components, and reserving partial discharge current pulse signal components; and (4) combining the denoised wavelet high-frequency component of each layerand the original wavelet low-frequency component of the highest layer through inverse discrete wavelet transform, and reconstructing a denoised partial discharge current pulse signal. The wavelet denoising method effectively removes interference in direct-current partial discharge, improves the detection precision and precision, is simple and rapid in calculation, and is easy to implement.

A method inverse discrete wavelet transforms subband data in segments and maintains a state between segments. The method selects one of a plurality of different computational procedures for performing the inverse DWT by testing the state and the subset of the current segment of subband data to determine if a current segment can be inverse transformed with a reduced computation procedure. If the test is positive the method performs the inverse DWT using the reduced computation procedure; otherwise the method performs the inverse DWT of the segment using another procedure.

A system is provided to realize suppression by selecting wavelets for feature compression in distributed speech recognition. The system comprises a first device and a second device. The first device comprising: a first network module for connecting to a network; an acoustic transducer module for recording speech and outputting frames of recorded signal; and a first processor configured for the following: extracting multiple-dimensional speech features from the frames of the recorded signal to generate multiple feature sequences; applying discrete wavelet transform (DWT) to the feature sequences to obtain a plurality of component data; and transmitting at least one of the plurality of component data via the network, wherein another one of the plurality of component data is not transmitted. The second device comprising: a second network module for connecting to the network and receiving the at least one of the plurality of component data from the first device; and a second processor configured for the following: updating the received data to generate an updated data; and applying inverse discrete wavelet transform (IDWT) to the updated data to obtain reconstructed speech data.

The invention discloses an audio tampering detection and recovery method combining compressed sensing and DWT. The method comprises the following steps: framing an original audio; extracting a DCT coefficient of each frame; connecting the compressed coefficients of each frame; performing linear transformation to obtain an unquantized reference value; calculating a matrix A and changing the floating point type watermark information into an integer to obtain watermark information to be embedded; embedding the watermark information into a high-frequency region of discrete wavelet transform of theoriginal audio; watermark information is extracted, whether the audio information is tampered is judged, and a tampered area is positioned; discarding the watermark information of the tampered area,extracting the effective watermark information of the area which is not tampered, and obtaining an approximate value of the quantized reference value. Therefore, the compressed information of the damaged area is obtained, decompressed, subjected to inverse discrete wavelet transform and connected with the undamaged area, and a recovered voice signal is obtained. According to the method, the peak signal-to-noise ratio of the embedded watermark information and the intelligibility of the audio after tampering recovery are improved, the calculation process is simpler, and tampering positioning ismore accurate.