CN108093264A - Core image compression, decompressing method and the system perceived based on splits' positions - Google Patents
Core image compression, decompressing method and the system perceived based on splits' positions Download PDFInfo
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Abstract
The present invention relates to a kind of core image compression, decompressing method and system perceived based on splits' positions, wherein, compression method includes：For core image to be compressed, the wavelet transformation rate of sampling at different levels is calculated according to the target sampling rate, decomposed class and code check of setting；Gaussian random matrix at different levels is obtained according to the piecemeal size of setting；Wavelet transform is carried out to core image to be compressed using discrete small wave converting method, according to subband and piecemeal size by each subband piecemeal of every level-one, obtains image block；Each image block of every level-one using gaussian random matrix is observed, obtains observation block；Lowest frequency subband in observation block is encoded using DPCM method；Lifting wavelet transform is carried out to high-frequency sub-band at different levels in observation block, then is encoded using set partitioning embedding space matrix method.The experimental results showed that the present invention can be effectively retained the textural characteristics of core image, improve image reconstruction quality under the conditions of high compression ratio.
Description
Technical field
The present invention relates to technical field of image processing more particularly to a kind of core image compressions perceived based on splits' positions
Method and system and core image decompressing method and system based on splits' positions perception.
Background technology
Rock core is basic geological data important in oilfield prospecting developing, and observation description is heavy in definite lithology, deduction
Play an important roll in product environment and source-reservoir-seal assemblage research.With widely using for core image acquisition equipment, by rock core
Sample is stored in digital form by scan mode, it has also become the important content of oil field digital Construction.Due to accumulation for many years
It constantly newly cores, causes core data amount extremely huge.Therefore, the compression algorithm for studying suitable core image feature is very
Significant.By being found to a large amount of typical core graphical analyses, since special geological environment and complicated geology transition are led
Cause core image generally have the characteristics that texture information enrich, contrast it is weaker.Traditional image compression algorithm based on small echo
Since the unusual limitation of the higher-dimensions such as wavelet representation edge, profile is extremely difficult to preferable compression effectiveness.By Donoho,
CS (Compressed Sensing, compressed sensing) theory that Candes and scientist Tao of Chinese origin et al. are proposed shows by asking
Optimization problem is solved, sparse signal can obtain the Exact recovery of high probability from a small amount of non-adaptive linear projection, this is
It further promotes compression of images performance and provides new technological means, cause the extensive attention of researcher.
In view of the calculation amount directly using CS methods reconstruct entire image is quite huge, Gan proposes BCS (Block
Compressed Sensing, splits' positions perceive) method, entire image is divided into the block of equidimension, independently to image block
It is observed and reconstructs, greatly reduce storage and calculate cost, but blocking effect can be generated in low bit- rate.Mun et al. is carried
Go out BCS-SPL (Smooth Projected Landweber, smoothly project Landweber) algorithm, pass through gaussian random matrix
It realizes sampling, is realized and reconstructed using Wiener filtering combination Landweber iterative algorithms, improve blocking effect, but reconstructed image is thin
Section becomes more fuzzy.So on the basis of BCS-SPL algorithms, James et al. proposes MS-BCS-SPL (Multiscale
Block Compressed Sensing with Smoothed Projected Landweber, multiple dimensioned piecemeal variable sampling rate
Compressed sensing algorithm) algorithm, by conversion point in DWT (Discrete Wavelet Transform, wavelet transform) domain
The every level subbands obtained after solution carry out piecemeal, then calculate the sub- rate of sampling and are sampled, then are combined by Wiener filtering
Landweber iteration realizes reconstruct, further improves the reconstruction quality of image.
According to stringent foundations of information theory frame, undertake data compression task mainly quantify and cataloged procedure, therefore
Compression ratio can be further improved by being compressed processing to observation using appropriate coding method.In the base that splits' positions perceive
On plinth, Sungkwang et al. is DPCM (Differential Pulse-Code Modulation, Differential Pulse Code Modulation)
It is combined with SQ (Scalar Quantization, uniform scalar quantization) and compression of images perception observation is quantified, money journey etc.
People combines DPCM and non-uniform scalar quantification treatment compression of images perceives observation, but is similar to this kind of texture letter of rock core in processing
Effect is not ideal enough during the image that breath is abundant and contrast is weaker.
The content of the invention
The technical problem to be solved by the present invention is to for existing method for compressing image, when handling core image, effect is not
The defects of enough preferable, provides a kind of core image compression, decompressing method and system perceived based on splits' positions.
First aspect present invention provides a kind of core image compression method perceived based on splits' positions, including：
Step 1, for core image to be compressed, according to the target sampling rate S of settingt, decomposed class L and code check BrIt calculates
The sub- rate S of sampling of the l grades of wavelet transforml, l ∈ [1, L]；
Step 2, the piecemeal size B according to settingl, pass through [SlBlBl+1/2]×BlBlSize obtain l grades Gauss with
Machine matrix Φl；
Step 3 carries out the core image to be compressed L grade conversion using discrete small wave converting method, according to subband with
Each subband piecemeal of every level-one is obtained size as B by piecemeal sizel×BlImage block xi；
Step 4, each image block x to every level-oneiUse gaussian random matrix ΦlIt is observed, obtains observation block
yi；
Step 5, to observation block yiMiddle lowest frequency subband observation is encoded using DPCM method；
Step 6, to observation block yiIn high-frequency sub-band observations at different levels carry out lifting wavelet transforms, then using set partitioning
Embedding space matrix method is encoded.
In the core image compression method according to the present invention perceived based on splits' positions, it is preferable that the step
Rapid 1 comprises the following steps：
Step 1-1, compressed sensing sample rate S is calculated by the following formula：
Wherein N is the matrix dimension of core image to be compressed；
Step 1-2, compressed sensing decimation factor S is acquired by the following formula conversionf,
Where it is assumed that the sub- rate S of the base band of wavelet transform0For fully sampled rate, then S0=1, and S1=1, to avoid generation
One or more SlThe situation of ＞ 1, make it is l grades all in the case of Sl≤1；WlFor the weighting coefficient of l grades of subbands, Wl=16L-l+1；
Step 1-3, for l grades of subbands, according to its piecemeal size BlUse matrix ΦlIt is sampled, required sampling
Sub- rate SlIt can be calculated by the following formula：
Sl=WlSf。
In the core image compression method according to the present invention perceived based on splits' positions, it is preferable that the step
It is compiled after also standardizing in rapid 6 with tile cutting techniques to observation using set partitioning embedding space matrix method
Code.
In the core image compression method according to the present invention perceived based on splits' positions, it is preferable that the step
The step of standardizing described in rapid 6 with tile cutting techniques to observation includes：
Step 6-1, observation block y is determinediSize for r × c, calculate d0=r × c；
Step 6-2, to d0Evolution rounding, obtains d；
Step 6-3, the value range of d is judged：
(1) if d=2j, then by observation block yiDimensional transformations be d × d；
(2) if 2j＜ d ＜ 2j+1, then observation block yiDimensional transformations be r0×c0, wherein r0=2j, c0=2j+1, and
Perform step 6-4；
Step 6-4, by r0As the length of side, tile form is r0×r0, judge r0With c0Whether the remainder that is divided by is zero, is to sentence
Without common portion between fixed tile to be cut, in observation block yiOn that set partitioning is respectively adopted is embedding after burst cutting from left to right
Enter block coding method to be encoded；If r0With c0The remainder that is divided by is not zero, then judges there is common portion between tile to be cut, seeing
Measured value block yiOn from left to right burst cut, record common portion position, be respectively adopted set partitioning embedding space matrix method into
Row coding, takes common portion to do average, then is integrated into coding result.
Second aspect of the present invention provides a kind of core image compressibility perceived based on splits' positions, including：
Sub- rate computing module is sampled, for being directed to core image to be compressed, according to the target sampling rate S of settingt, decomposition level
Number L and code check BrCalculate the sub- rate S of sampling of the l grades of wavelet transforml, l ∈ [1, L]；
Matrix computations module, for the piecemeal size B according to settingl, pass through [SlBlBl+1/2]×BlBlSize obtain l
The gaussian random matrix Φ of gradel；
Wavelet transformation module, for carrying out L grades of conversion to the core image to be compressed using discrete small wave converting method,
According to subband and piecemeal size by each subband piecemeal of every level-one, size is obtained as Bl×BlImage block xi；
Observation computing module, for each image block x to every level-oneiUse gaussian random matrix ΦlIt is observed,
Obtain observation block yi,
First coding module, for observation block yiMiddle lowest frequency subband observation uses Differential Pulse Code Modulation side
Method is encoded；
Second coding module, for observation block yiIn high-frequency sub-band observations at different levels carry out lifting wavelet transforms, then
It is encoded using set partitioning embedding space matrix method.
In the core image compressibility according to the present invention perceived based on splits' positions, it is preferable that described to adopt
Appearance rate computing module includes：
First computing unit, for calculating compressed sensing sample rate S by the following formula：
Wherein N is the matrix dimension of core image to be compressed；
Second computing unit, for acquiring compressed sensing decimation factor S by the following formula conversionf,
Where it is assumed that the sub- rate S of the base band of wavelet transform0For fully sampled rate, then S0=1, and S1=1, to avoid generation
One or more SlThe situation of ＞ 1, make it is l grades all in the case of Sl≤1；WlFor the weighting coefficient of l grades of subbands, Wl=16L-l+1；
3rd computing unit, for for l grades of subbands, according to its piecemeal size BlUse matrix ΦlIt is sampled, institute
The sub- rate S of sampling neededlIt can be calculated by the following formula：
Sl=WlSf。
In the core image compressibility according to the present invention perceived based on splits' positions, it is preferable that described the
Two coding modules include normalization unit, for using set partitioning after standardizing with tile cutting techniques to observation
Embedding space matrix method is encoded.
In the core image compressibility according to the present invention perceived based on splits' positions, it is preferable that the rule
Generalized unit includes：
First processing subelement, for determining observation block yiSize for r × c, calculate d0=r × c；
Second processing subelement, for d0Evolution rounding, obtains d；
3rd processing subelement, for judging the value range of d：
(1) if d=2j, then by observation block yiDimensional transformations be d × d；
(2) if 2j＜ d ＜ 2j+1, then observation block yiDimensional transformations be r0×c0, wherein r0=2j, c0=2j+1, and
Start fourth process subelement；
Fourth process subelement, by r0As the length of side, tile form is r0×r0, judge r0With c0The remainder that is divided by whether be
Zero, it is to judge between tile to be cut without common portion, in observation block yiOn from left to right burst cutting after be respectively adopted
Set partitioning embedding space matrix method is encoded；If r0With c0The remainder that is divided by is not zero, then judges to have between tile to be cut public
Part, in observation block yiOn from left to right burst cut, set partitioning embedded block volume is respectively adopted in record common portion position
Code method is encoded, and common portion is taken to do average, then is integrated into coding result.
Third aspect present invention provides a kind of core image decompressing method perceived based on splits' positions, including：
Step 1 carries out DPCM decodings to the lowest frequency subband observation for compressing image；
Step 2 carries out SPECK decodings and Lifting Wavelet inverse transformation to the high-frequency sub-band observations at different levels for compressing image；
The observation of step 3, each sub-block of at different levels high-frequency sub-bands of the integration through SPECK decoding specifications, makes its extensive
Restore the size of beginning core image；
Step 4 seeks approximate solution using Minimum Mean Squared Error estimation, so as to obtain the initial solution of reconstructed image
Step 5, the initial solution to the reconstructed imageOptimize to obtain optimal solution.
Fourth aspect present invention provides a kind of core image decompression system perceived based on splits' positions, including：
First decoder module, for carrying out DPCM decodings to the lowest frequency subband observation for compressing image；
Second decoder module, for carrying out SPECK decodings to the high-frequency sub-band observations at different levels for compressing image and being promoted small
Ripple inverse transformation.
Size restoration module, for integrating the sight of each sub-block of the high-frequency sub-bands at different levels through SPECK decoding specifications
Measured value makes it recover the size of original core image；
Initial solution computing module, for seeking approximate solution using Minimum Mean Squared Error estimation, so as to obtain the first of reconstructed image
Begin solution
Optimization module, for the initial solution to the reconstructed imageOptimize to obtain optimal solution.
The above-mentioned technical proposal for implementing the present invention has the following advantages that：The present invention is using wavelet transform to core image
Rarefaction representation is carried out, multiple dimensioned piecemeal is carried out to each subband, subband not at the same level distributes different sample rates, with orthogonal gaussian random
Matrix is observed the image block of corresponding level, and lowest frequency subband observation is encoded using DPCM, high-frequency sub-band observation warp
Lifting wavelet transform carries out SPECK codings, and compression and reconstruct are realized by Wiener filtering combination Landweber iteration；Experiment knot
Fruit shows under conditions of high compression ratio, with using DPCM combinations uniform scalar quantization and directly using uniform scalar quantization side
Method is compared, and method proposed by the present invention effectively remains the textural characteristics of core image, improves regarding for reconstruct core image
Feel effect and Y-PSNR.
Description of the drawings
Fig. 1 is the flow according to the core image compression method perceived based on splits' positions of the preferred embodiment of the present invention
Figure；
Fig. 2 a and Fig. 2 b are respectively the core image compression according to the preferred embodiment of the invention perceived based on splits' positions
The two kinds of tile slit modes used in method；
Fig. 3 is the module frame chart according to the core image compressibility perceived based on splits' positions of the present invention；
Fig. 4 a~4d give the quality reconstruction pair that 3 width core images when code check is 0.0841bpp apply 3 kinds of compression methods
Than figure；
Fig. 5 a~5d give the quality reconstruction pair that 3 width core images when code check is 0.0802bpp apply 3 kinds of compression methods
Than figure；
Fig. 6 a~6d give the quality reconstruction pair that 3 width core images when code check is 0.0919bpp apply 3 kinds of compression methods
Than figure；
Fig. 7 a~7c are respectively the property of core image 1, core image 2 and core image 3 using 3 kinds of method compressions and reconstruct
It can comparison.
Specific embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, the technical solution in the embodiment of the present invention is clearly and completely described.Obviously, described embodiment is
The part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people
Member's all other embodiments obtained on the premise of creative work is not made, belong to the scope of protection of the invention.
The present invention proposes the DPCM (Differential perceived based on splits' positions according to the characteristics of core image
Pulse-Code Modulation, Differential Pulse Code Modulation) and SPECK (Set Partitioned Embedded
Block, set partitioning embedding space matrix) Compression Strategies that are combined of method.In DWT (Discrete Wavelet
Transform) in domain, the every level-one low frequency obtained after decomposition will be converted and high-frequency sub-band carries out piecemeal, then according to sample rate
Observation is obtained with observing matrix.DPCM codings are individually carried out to lowest frequency subband observation, high-frequency sub-band observation is carried out
Lifting wavelet transform (Lifting Wavelet Transform), makes energy carry out SPECK encoding and decoding again after further concentrating.
The compression and reconstruct of core image are realized finally by Wiener filtering combination Landweber iteration.
Referring to Fig. 1, it is the core image compression method perceived based on splits' positions according to the preferred embodiment of the present invention
Flow chart.As shown in Figure 1, the core image compression method perceived based on splits' positions that the embodiment provides includes following step
Suddenly：
First, in step S101, for core image to be compressed, according to the target sampling rate S of settingt, decomposed class L
With code check BrCalculate the sub- rate S of sampling of the l grades of wavelet transforml, wherein, l ∈ [1, L].
Then, in step s 102, according to the piecemeal size B of settingl, calculate the sub- rate S of sampling at different levelsl, pass through [SlBlBl
+1/2]×BlBlObtain with its etc. sizes l grade gaussian random matrixes Φl。
Then, in step s 103, L is carried out to the core image to be compressed using discrete small wave converting method (DWT)
Grade conversion according to subband and piecemeal size by each subband piecemeal of every level-one, obtains size as Bl×BlImage block xi。
Then, in step S104, to each image block x of every level-oneiUse gaussian random matrix ΦlIt is observed, obtains
To observation block yi, i.e. yi=Φlxi。
Then, in step S105, to observation block yiMiddle lowest frequency subband observation uses Differential Pulse Code Modulation
Method (DPCM) is encoded, and obtains the lowest frequency subband observation of compression image
Then, in step s 106, to observation block yiIn high-frequency sub-band observations at different levels carry out lifting wavelet transforms, then
It is encoded using set partitioning embedding space matrix method (SPECK), obtains the high-frequency sub-band observations at different levels of compression imageThis method make full use of wavelet coefficient energy collection neutralize energy with scale increase and the characteristics of decay, by quaternary tree division and
Bit-plane coding method is combined, and has higher compression performance.Preferably, Lifting Wavelet change is being carried out in step S106
After changing, encoded again using SPECK after also standardizing with tile cutting techniques to observation.The present invention does not limit
The order of step S105 and step S106.
Preferably, above-mentioned steps S101 comprises the following steps：
(1) compressed sensing sample rate S is calculated by the following formula：
Wherein N is the matrix dimension of core image to be compressed；
(2) compressed sensing decimation factor S is acquired by the following formula conversionf,
Where it is assumed that the sub- rate S of the base band of wavelet transform0For fully sampled rate, then S0=1, and S1=1, to avoid generation
One or more SlThe situation of ＞ 1, make it is l grades all in the case of Sl＞ 1.WlFor the weighting coefficient of l grades of subbands, and：
Wl=16L-l+1； (3)
(3) for l grades of subbands, according to its piecemeal size BlUse matrix ΦlIt is sampled, the required sub- rate of sampling
SlIt can be calculated by the following formula：
Sl=WlSf。 (4)
Table 1 is given in different target sampling rate StUnder, the sub- rate S of sampling of the l grades of the DWT conversion realizations of L=3lSystem
Meter.
Table 1
The principle to the core image compression method perceived based on splits' positions of the invention and process are carried out detailed below
Explanation.
1st, the multiple dimensioned splits' positions based on small echo perceive
According to compressive sensing theory, it is assumed that x is the signal that the length that is obtained from M sampled signal y is N, and M ＜ ＜ N,
It is possible to recover signal x from formula (5).
Y=Φ x, (5)
Wherein, x ∈ RN, y ∈ RM, i.e. x is a N-dimensional vector, and y is a M dimensional vector (i.e. observation), and Φ is a tool
There is the observing matrix for M × N-dimensional that sample rate is S=M/N.
To solve the problems, such as directly to use CS method reconstructed images computationally intensive, Gan proposes splits' positions cognitive method.It is false
If the image x that a width size is N × N is divided into the image block that size is B × B, the vector of i-th of image block represents to be denoted as xi,
Use observing matrix ΦBIt is sampled, obtains observation：
yi=ΦBxi, (6)
Wherein, the size of B is integrated and determined according to the rate of image reconstruction and the quality requirement of reconstruct：When B is smaller,
EMS memory occupation is few and calculating speed is fast；When B is larger, the quality reconstruction of image is relatively good；I=1 ... n, n=N2/B2；ΦB
It is that size is MB×B2Orthogonal observing matrix, MB=(M × B2)/N2, M is the observed samples value number of entire image.
Since core image texture information enriches, to retain more edges and detailed information, the present invention is proposed based on more
Scale splits' positions perceive observation code compression method, i.e., first carry out wavelet transform to core image before piecemeal, so
Observing matrix ΦBIt is divided into two parts：DWT multi-scale transform matrix Ω and multiple dimensioned piecemeal observing matrix Φl, i.e. yi=Φl
Ωxi.Assuming that Ω is L grades of DWT decomposition transform matrixes, then, ΦlIt is made of L+1 different observing matrixes.At this moment, image x
L grades be divided into size for Bl×BlImage block, subband passes through observing matrix ΦlIt is sampled, obtaining corresponding size isObservation block yi,Wherein, l ∈ [1, L].On this basis, to the core image of acquisition
Splits' positions perceive observation and are encoded and then realize the compression of core image.
2nd, the compression of the core image observation perceived based on multiple dimensioned splits' positions
2.1 compressed sensing observation standardization processings
Core image is after multiple dimensioned splits' positions perceive sampling observation, between lowest frequency subband observation adjacent block
Correlation is stronger, i.e. the data redundancy of lowest frequency subband observation is larger.And most energy of core image concentrate on
Lowest frequency subband accordingly, it is capable to which no carry out efficient coding to lowest frequency subband observation, will largely influence image coding
Quality.And correlation is weaker between high-frequency sub-band observation adjacent block, data redundancy is little, and the energy contained is also relatively
It is few, but include more detailed information.Therefore the present invention considers to encode lowest frequency subband observation using two dimension DPCM, and right
High-frequency sub-band observation is then encoded using the SPECK algorithms with preferable compression performance based on block structure.
The observation that usual core image is perceived through multiple dimensioned splits' positions is that irregular, traditional SPECK is calculated
Method can only handle the matrix of rule, be carried out again after carrying out standardization processing present invention preferably employs tile cutting techniques therefore
SPECK is encoded.The operation of tile models coupling piecemeal can effectively reduce data volume to be treated, make up the deficiency of wavelet transformation,
It can reach better effects for operations such as compression of images.The basic step for observation of standardizing in step S106 is as follows：
Step 1：Determine observation block yiSize for r × c, calculate d0=r × c；
Step 2：To d0Evolution rounding, obtains d；
Step 3：Judge the value range of d：
(1) if d=2j, then by observation block yiDimensional transformations be d × d；It can carry out SPECK encoding operations；
(2) if 2j＜ d ＜ 2j+1, then observation block yiDimensional transformations be r0×c0, wherein r0=2j, c0=2j+1, j ∈
Z+；
Step 4：If meet the situation (2) of above-mentioned steps 3, by r0As the length of side, tile form is r0×r0：
(1) if r0With c0The remainder that is divided by is zero, then can be determined that between tile to be cut without common portion, in observation
SPECK codings are done respectively after burst cutting from left to right.Sliced fashion is as shown in Figure 2 a.
(2) if r0With c0The remainder that is divided by is not zero, then can be determined that between tile to be cut there is common portion, in observation
Burst is cut from left to right, and record common portion position carries out SPECK codings, common portion is taken to do average, then is integrated into respectively
SPECK coding resultsSliced fashion is as shown in Figure 2 b.
2.2 core image compressed sensing observation DPCM+SPECK are encoded
Using the graded characteristics of wavelet conversion coefficient, multiple dimensioned piecemeal is carried out to each subband of core image, and is keeping mesh
In the case that mark sample rate is constant, different fractions match somebody with somebody different sample rates, calculate corresponding observing matrix.With observing matrix to phase
The image block of level is answered to be observed, obtains observation.DPCM codings are carried out to lowest frequency subband observation, high-frequency sub-band is seen
Measured value carries out tile dividing processing and carries out lifting wavelet transform first, is then encoded using SPECK.Finally by Wiener filtering
The compression and reconstruct to core image are realized with reference to Landweber iterative operations.Therefore, core image observation pressure of the invention
Contracting method and step such as abovementioned steps S101~S106.
Referring to Fig. 3, the module frame chart for the core image compressibility perceived based on splits' positions according to the present invention.
As shown in figure 3, the core image compressibility perceived based on splits' positions that the embodiment provides includes sampling sub- rate calculating mould
Block 301, matrix computations module 302, wavelet transformation module 303, observation computing module 304, the first coding module 305 and second
Coding module 306.
Wherein, sub- rate computing module 301 is sampled for for core image to be compressed, according to the target sampling rate of setting
St, decomposed class L and code check BrCalculate the sub- rate S of sampling of the l grades of wavelet transforml, wherein, l ∈ [1, L].
Matrix computations module 302 is used for the piecemeal size B according to settingl, pass through [SlBlBl+1/2]×BlBlObtain and its
Etc. the l grades of sizes gaussian random matrix Φl。
Wavelet transformation module 303 is used to carry out L grades of changes to the core image to be compressed using discrete small wave converting method
It changes, according to subband and piecemeal size by each subband piecemeal of every level-one, obtains size as Bl×BlImage block xi。
Observation computing module 304 is used for each image block x to every level-oneiUse gaussian random matrix ΦlIt is seen
It surveys, obtains observation block yi；
First coding module 305 is connected with the observation computing module 304, for observation block yiMiddle lowest frequency
Band observation is encoded using DPCM method.
Second coding module 306 is connected with the observation computing module 304, for observation block yiIn high frequencies at different levels
Subband observation carries out lifting wavelet transform, then is encoded using set partitioning embedding space matrix method.
Preferably, sampling sub- rate computing module 301 includes：
First computing unit, for calculating compressed sensing sample rate S by the following formula：
Wherein N is the matrix dimension of core image to be compressed；
Second computing unit acquires compressed sensing decimation factor S by the following formula conversionf,
Where it is assumed that the sub- rate S of the base band of wavelet transform0For fully sampled rate, then S0=1, and S1=1, to avoid generation
One or more SlThe situation of ＞ 1, make it is l grades all in the case of Sl＜ 1；WlFor the weighting coefficient of l grades of subbands, Wl=16L-l+1；
3rd computing unit, for in l grades of subbands, according to its piecemeal size BlUse matrix ΦlIt is sampled,
The required sub- rate S of samplinglIt can be calculated by the following formula：
Sl=WlSf。
Preferably, the second coding module 306 includes normalization unit, for being carried out with tile cutting techniques to observation
It is encoded again using set partitioning embedding space matrix method after standardization.
Preferably, the normalization unit includes：
First processing subelement, for determining observation block yiSize for r × c, calculate d0=r × c；
Second processing subelement, for d0Evolution rounding, obtains d；
3rd processing subelement, for judging the value range of d：
(1) if d=2j, then by observation block yiDimensional transformations be d × d；
(2) if 2j＜ d ＜ 2j+1, then observation block yiDimensional transformations be r0×c0, wherein r0=2j, c0=2j+1, and
Start fourth process subelement；
Fourth process subelement, when the 3rd processing subelement judges that the value range of d plants situation for (2), for inciting somebody to action
r0As the length of side, tile form is r0×r0, judge r0With c0Whether the remainder that is divided by is zero, is to judge no common portion,
Observation block yiOn set partitioning embedding space matrix method be respectively adopted encoded after burst cutting from left to right；If r0With c0
The remainder that is divided by is not zero, then is determined with common portion, in observation block yiOn from left to right burst cut, record common portion position
It puts, set partitioning embedding space matrix method is respectively adopted and is encoded, common portion is taken to do average, then is integrated into coding result
The present invention also provides a kind of core image decompressing methods perceived based on splits' positions, and decompression procedure is compressed
The inverse process of journey is first decoded observation operation, and recovers full size size, then in conjunction with Wiener filtering and
Landweber iteration realizes core image reconstruct, is as follows：
Step 1, the lowest frequency subband observation to compressing imageCarry out DPCM decodings；
Step 2, the high-frequency sub-band observations at different levels to compressing imageCarry out SPECK decodings (the inverse mistake of SPECK codings
Journey) and Lifting Wavelet inverse transformation；
The observation of step 3, each sub-block of at different levels high-frequency sub-bands of the integration through SPECK decoding specifications, makes its extensive
The size of beginning core image is restored, i.e.,Size；
Step 4 solves x using MMSE (Minimum Mean Square Error, Minimum Mean Squared Error estimation)iIt is near
Like solution, so as to obtain the initial solution of reconstructed image
Step 5, the initial solution to the reconstructed imageOptimize to obtain optimal solution.
Preferably, the detailed process optimized in the step 5 is as follows：
(1) blocking effect caused by the adaptive wiener filter removal image block of 3 × 3 neighborhoods；
(2) by filtered image projection in convex set, since observing matrix is orthogonal matrix, can be obtained by following formula：
Wherein,For the initial solution of the reconstructed image, ΦBFor orthogonal observing matrix, yiFor observation；
(3) projection result is carried out wavelet transformation, projection result is filtered using bivariate shrinkage function contraction in wavelet field
Ripple；
(4) inverse wavelet transform is carried out to filter result, it will be on filtered image reprojection to convex set；
(5) judge and terminate iteration, until obtaining optimal solution.
The present invention further correspondingly provides a kind of core image decompression system perceived based on splits' positions, including：
First decoder module, for the lowest frequency subband observation to compressing imageCarry out DPCM decodings；
Second decoder module, for the high-frequency sub-band observations at different levels to compressing imageCarry out SPECK decodings (SPECK
The inverse process of coding) and Lifting Wavelet inverse transformation；
Size restoration module, for integrating the sight of each sub-block of the high-frequency sub-bands at different levels through SPECK decoding specifications
Measured value makes it recover the size of original core image；
Initial solution computing module, for seeking approximate solution using Minimum Mean Squared Error estimation, so as to obtain the first of reconstructed image
The solution that begins xi；
Optimization module, for the initial solution x to the reconstructed imageiOptimize to obtain optimal solution.Wherein optimization module
Optimization method is identical with step 5 in foregoing decompressing method, and details are not described herein.
The present invention is compressed the core image that is perceived based on splits' positions by following contrast experiment, decompressing method and system
It is verified.
Wherein, the core image compression method of the invention perceived based on splits' positions is as previously mentioned, i.e. at multiple dimensioned point
DPCM and SPECK algorithms are combined on the basis of block compressed sensing algorithm, encoding and decoding are carried out to core image observation, realize rock core
The compression and reconstruct of image.Control methods 1 is using referring in background technology DPCM (Differential Pulse-Code
Modulation, Differential Pulse Code Modulation) and the sides that are combined of SQ (Scalar Quantization, uniform scalar quantization)
Method, labeled as MS-BCS-SPL-DPCM+SQ；Control methods 2 is using without predicting directly using uniform scalar quantization (SQ)
Method, labeled as MS-BCS-SPL-SQ.
3 512 × 512 more representational core images are tested respectively using above-mentioned 3 kinds of methods in experiment,
Selected wavelet transform filter is 9/7 integer wavelet, and conversion series is 3, and observing matrix uses orthogonal gaussian random matrix, sparse
Base sets wavelet transforms (Duel-tree Discrete Wavelet Transform, DDWT) using 3 grades pairs, per level-one
Image block size is respectively 8,16,32.Under same quantization method under identical reconstruction model, the height of image reconstruction quality
It is low to depend on sample rate and quantizing bit number, sample rate and quantizing bit number are reasonably set, test out different samplings
The Y-PSNR of core image is reconstructed under rate and quantizing bit number.
2 core image of table applies the PSNR (dB) of 3 kinds of method reconstructed images
Note：S in form 2tFor target sampling rate.
Table 2 gives the PSNR values that 3 width core images realize compression and reconstruct using 3 kinds of algorithms respectively.As can be seen that
In the case of high compression ratio, compressed using the method for the present invention core image with the PSNR values reconstructed than being carried using other 2 kinds of methods
High 0.3~1.0dB.Fig. 4 a~4d give the weight that 3 width core images when code check is 0.0841bpp apply 3 kinds of compression methods
Structure effect contrast figure.Wherein Fig. 4 a are original core image 1；Fig. 4 b are to be obtained using the i.e. MS-BCS-SPL-SQ of control methods 2
Reconstructed image, PSNR=26.64；It using control methods 1 is the obtained reconstructed images of MS-BCS-SPL-DPCM+SQ that Fig. 4 c, which are,
PSNR=26.65；Fig. 4 d are the reconstructed image that is obtained using the method for the present invention, PSNR=27.16.Fig. 5 a~5d give code check
For 0.0802bpp when 3 width core images using 3 kinds of compression methods quality reconstruction comparison diagram.Wherein Fig. 5 a are original rock core
Image 2；Fig. 5 b are using the reconstructed image that i.e. MS-BCS-SPL-SQ is obtained of control methods 2, PSNR=23.25；Fig. 5 c are use
The reconstructed image that i.e. MS-BCS-SPL-DPCM+SQ is obtained of control methods 1, PSNR=23.25；Fig. 4 d are to use the method for the present invention
Obtained reconstructed image, PSNR=23.86.Fig. 6 a~6d give 3 width core images when code check is 0.0919bpp and apply 3 kinds
The quality reconstruction comparison diagram of compression method.Wherein Fig. 6 a are original core image 3；Fig. 6 b are using control methods 2 i.e. MS-
The reconstructed image that BCS-SPL-SQ is obtained, PSNR=20.03；Fig. 6 c are using control methods 1 i.e. MS-BCS-SPL-DPCM+SQ
Obtained reconstructed image, PSNR=20.03；Fig. 6 d are the reconstructed image that is obtained using the method for the present invention, PSNR=20.38.Its
For middle core image 3 compared with 2 texture-rich of core image, and for core image 1,2 texture of core image is again relatively abundant.It can
To find out, better vision is shown when handling the image of texture information relative abundance using method proposed by the present invention and imitated
Fruit remains more texture informations.
Fig. 7 a~7c are respectively the property of core image 1, core image 2 and core image 3 using 3 kinds of method compressions and reconstruct
It can compare, can more intuitively find out variation tendency and the present invention side of PSNR value of 3 kinds of methods under different compression ratios
Method is compared to the promotion degree of the PSNR values of other 2 kinds of methods.
In conclusion the characteristics of herein according to core image, on the basis of multiple dimensioned splits' positions perceive framework, fully
The characteristics of using SPECK and DPCM encryption algorithms, carry out a large amount of emulation experiments and analysis, realize to core image observation into
One step is compressed and reconstruct, obtained core image visual effect and Y-PSNR are promoted, with being tied using SQ and DPCM
It closes SQ to compare, PSNR values averagely improve 0.6dB and 0.5dB respectively, and in the case of high compression ratio, have been effectively retained rock core
The textural characteristics of image.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that：It still may be used
To modify to the technical solution recorded in foregoing embodiments or carry out equivalent substitution to which part technical characteristic；
And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical solution spirit and
Scope.
Claims (10)
1. a kind of core image compression method perceived based on splits' positions, which is characterized in that including：
Step 1, for core image to be compressed, according to the target sampling rate S of settingt, decomposed class L and code check BrIt calculates discrete
The sub- rate S of sampling of the l grades of wavelet transformationl, wherein, l ∈ [1, L]；
Step 2, the piecemeal size B according to settingl, pass through [SlBlBl+1/2]×BlBlSize obtain l grades of gaussian random square
Battle array Φl；
Step 3 carries out L grades of conversion using discrete small wave converting method to the core image to be compressed, according to subband and piecemeal
Each subband piecemeal of every level-one is obtained size as B by sizel×BlImage block xi；
Step 4, each image block x to every level-oneiUse gaussian random matrix ΦlIt is observed, obtains observation block yi；
Step 5, to observation block yiMiddle lowest frequency subband observation is encoded using DPCM method；
Step 6, to observation block yiIn high-frequency sub-band observations at different levels carry out lifting wavelet transforms, then be embedded in using set partitioning
Block coding method is encoded.
2. the core image compression method according to claim 1 perceived based on splits' positions, which is characterized in that the step
Rapid 1 comprises the following steps：
Step 1-1, compressed sensing sample rate S is calculated by the following formula：
<mrow>
<mi>S</mi>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>S</mi>
<mi>t</mi>
</msub>
<mi>N</mi>
<mi>N</mi>
</mrow>
<mrow>
<msub>
<mi>B</mi>
<mi>r</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>N</mi>
<mi>N</mi>
<mo>-</mo>
<msup>
<mn>2</mn>
<mi>L</mi>
</msup>
<msup>
<mn>2</mn>
<mi>L</mi>
</msup>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msup>
<mn>2</mn>
<mi>L</mi>
</msup>
<msup>
<mn>2</mn>
<mi>L</mi>
</msup>
</mrow>
</mfrac>
<mo>,</mo>
</mrow>
Wherein N is the matrix dimension of core image to be compressed；
Step 1-2, compressed sensing decimation factor S is acquired by the following formula conversionf,
<mrow>
<mi>S</mi>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<msup>
<mn>4</mn>
<mi>L</mi>
</msup>
</mfrac>
<msub>
<mi>S</mi>
<mn>0</mn>
</msub>
<mo>+</mo>
<mfrac>
<mn>3</mn>
<msup>
<mn>4</mn>
<mi>L</mi>
</msup>
</mfrac>
<msub>
<mi>S</mi>
<mn>1</mn>
</msub>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>l</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>L</mi>
</munderover>
<mfrac>
<mn>3</mn>
<msup>
<mn>4</mn>
<mrow>
<mi>L</mi>
<mo>-</mo>
<mi>l</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msup>
</mfrac>
<msub>
<mi>W</mi>
<mi>l</mi>
</msub>
<msub>
<mi>S</mi>
<mi>f</mi>
</msub>
<mo>,</mo>
</mrow>
Where it is assumed that the sub- rate S of the base band of wavelet transform0For fully sampled rate, then S0=1, and S1=1, to avoid generating one
Or multiple SlThe situation of ＞ 1, make it is l grades all in the case of Sl≤1；WlFor the weighting coefficient of l grades of subbands, Wl=16L-l+1；
Step 1-3, for l grades of subbands, according to its piecemeal size BlUse matrix ΦlIt is sampled, the required sub- rate of sampling
SlIt is calculated by the following formula：
Sl=WlSf。
3. the core image compression method according to claim 1 perceived based on splits' positions, which is characterized in that the step
It is compiled after also standardizing in rapid 6 with tile cutting techniques to observation using set partitioning embedding space matrix method
Code.
4. the core image compression method according to claim 3 perceived based on splits' positions, which is characterized in that the step
The step of standardizing described in rapid 6 with tile cutting techniques to observation includes：
Step 6-1, observation block y is determinediSize for r × c, calculate d0=r × c；
Step 6-2, to d0Evolution rounding, obtains d；
Step 6-3, the value range of d is judged：
(1) if d=2j, then by observation block yiDimensional transformations be d × d；
(2) if 2j＜ d ＜ 2j+1, then observation block yiDimensional transformations be r0×c0, wherein r0=2j, c0=2j+1, and perform step
Rapid 6-4；
Step 6-4, by r0As the length of side, tile form is r0×r0, judge r0With c0Whether the remainder that is divided by is zero, is, judgement is treated
Without common portion between cutting tile, in observation block yiOn from left to right burst cutting after set partitioning embedded block is respectively adopted
Coding method is encoded；If r0With c0The remainder that is divided by is not zero, then judges there is common portion between tile to be cut, in observation
Block yiOn from left to right burst cut, record common portion position is respectively adopted set partitioning embedding space matrix method and is compiled
Code, takes common portion to do average, then is integrated into coding result.
5. a kind of core image compressibility perceived based on splits' positions, which is characterized in that including：
Sub- rate computing module is sampled, for being directed to core image to be compressed, according to the target sampling rate S of settingt, decomposed class L and
Code check BrCalculate the sub- rate S of sampling of the l grades of wavelet transforml, l ∈ [1, L]；
Matrix computations module, for the piecemeal size B according to settingl, pass through [SlBlBl+1/2]×BlBlSize obtain l grades
Gaussian random matrix Φl；
Wavelet transformation module, for carrying out L grades of conversion to the core image to be compressed using discrete small wave converting method, according to
Each subband piecemeal of every level-one is obtained size as B by subband and piecemeal sizel×BlImage block xi；
Observation computing module, for each image block x to every level-oneiUse gaussian random matrix ΦlIt is observed, obtains
Observation block yi；
First coding module, for observation block yiMiddle lowest frequency subband observation using DPCM method into
Row coding；
Second coding module, for observation block yiIn high-frequency sub-band observations at different levels carry out lifting wavelet transforms, then using collection
Division embedding space matrix method is closed to be encoded.
6. the core image compressibility according to claim 5 perceived based on splits' positions, which is characterized in that described to adopt
Appearance rate computing module includes：
First computing unit, for calculating compressed sensing sample rate S by the following formula：
<mrow>
<mi>S</mi>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>S</mi>
<mi>t</mi>
</msub>
<mi>N</mi>
<mi>N</mi>
</mrow>
<mrow>
<msub>
<mi>B</mi>
<mi>r</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>N</mi>
<mi>N</mi>
<mo>-</mo>
<msup>
<mn>2</mn>
<mi>L</mi>
</msup>
<msup>
<mn>2</mn>
<mi>L</mi>
</msup>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msup>
<mn>2</mn>
<mi>L</mi>
</msup>
<msup>
<mn>2</mn>
<mi>L</mi>
</msup>
</mrow>
</mfrac>
<mo>,</mo>
</mrow>
Wherein N is the matrix dimension of core image to be compressed；
Second computing unit, for acquiring compressed sensing decimation factor S by the following formula conversionf,
<mrow>
<mi>S</mi>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<msup>
<mn>4</mn>
<mi>L</mi>
</msup>
</mfrac>
<msub>
<mi>S</mi>
<mn>0</mn>
</msub>
<mo>+</mo>
<mfrac>
<mn>3</mn>
<msup>
<mn>4</mn>
<mi>L</mi>
</msup>
</mfrac>
<msub>
<mi>S</mi>
<mn>1</mn>
</msub>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>l</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>L</mi>
</munderover>
<mfrac>
<mn>3</mn>
<msup>
<mn>4</mn>
<mrow>
<mi>L</mi>
<mo>-</mo>
<mi>l</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msup>
</mfrac>
<msub>
<mi>W</mi>
<mi>l</mi>
</msub>
<msub>
<mi>S</mi>
<mi>f</mi>
</msub>
<mo>,</mo>
</mrow>
Where it is assumed that the sub- rate S of the base band of wavelet transform0For fully sampled rate, then S0=1, and S1=1, to avoid generating one
Or multiple SlThe situation of ＞ 1, make it is l grades all in the case of Sl≤1；WlFor the weighting coefficient of l grades of subbands, Wl=16L-l+1；
3rd computing unit, for for l grades of subbands, according to its piecemeal size BlUse matrix ΦlIt is sampled, required for
The sub- rate S of samplinglIt is calculated by the following formula：
Sl=WlSf。
7. the core image compressibility according to claim 5 perceived based on splits' positions, which is characterized in that described the
Two coding modules include normalization unit, for using set partitioning after standardizing with tile cutting techniques to observation
Embedding space matrix method is encoded.
8. the core image compressibility according to claim 7 perceived based on splits' positions, which is characterized in that the rule
Generalized unit includes：
First processing subelement, for determining observation block yiSize for r × c, calculate d0=r × c；
Second processing subelement, for d0Evolution rounding, obtains d；
3rd processing subelement, for judging the value range of d：
(1) if d=2j, then by observation block yiDimensional transformations be d × d；
(2) if 2j＜ d ＜ 2j+1, then observation block yiDimensional transformations be r0×c0, wherein r0=2j, c0=2j+1, and start the
Four processing subelements；
Fourth process subelement, by r0As the length of side, tile form is r0×r0, judge r0With c0Whether the remainder that is divided by is zero, is
No common portion is then judged, in observation block yiOn from left to right burst cutting after set partitioning embedding space matrix is respectively adopted
Method is encoded；If r0With c0The remainder that is divided by is not zero, then is determined with common portion, in observation block yiOn divide from left to right
Piece is cut, and record common portion position is respectively adopted set partitioning embedding space matrix method and is encoded, common portion is taken to do
Value, then it is integrated into coding result.
9. a kind of core image decompressing method perceived based on splits' positions, which is characterized in that including：
Step 1 carries out DPCM decodings to the lowest frequency subband observation for compressing image；
Step 2 carries out SPECK decodings and Lifting Wavelet inverse transformation to the high-frequency sub-band observations at different levels for compressing image；
The observation of step 3, each sub-block of at different levels high-frequency sub-bands of the integration through SPECK decoding specifications, makes its recover former
The size of beginning core image；
Step 4 seeks approximate solution using Minimum Mean Squared Error estimation, so as to obtain the initial solution of reconstructed image
Step 5, the initial solution to the reconstructed imageOptimize to obtain optimal solution.
10. a kind of core image decompression system perceived based on splits' positions, which is characterized in that including：
First decoder module, for carrying out DPCM decodings to the lowest frequency subband observation for compressing image；
Second decoder module, for inverse to the high-frequency sub-band observations at different levels progress SPECK decodings and Lifting Wavelet of compressing image
Conversion；
Size restoration module, for integrating the observation of each sub-block of the high-frequency sub-bands at different levels through SPECK decoding specifications,
It is made to recover the size of original core image；
Initial solution computing module, for seeking approximate solution using Minimum Mean Squared Error estimation, so as to obtain the initial solution of reconstructed image
Optimization module, for the initial solution to the reconstructed imageOptimize to obtain optimal solution.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109559357A (en) * | 2018-09-17 | 2019-04-02 | 衡阳师范学院 | A kind of tile compression sensing reconstructing method based on wavelet packet threshold value |
| CN110175965A (en) * | 2019-05-30 | 2019-08-27 | 齐齐哈尔大学 | Based on splits' positions cognitive method that is adaptively sampled and smoothly projecting |
| CN110677655A (en) * | 2019-06-21 | 2020-01-10 | 杭州海康威视数字技术股份有限公司 | Encoding and decoding method, device and storage medium |
| CN111193925A (en) * | 2019-12-25 | 2020-05-22 | 杭州中威电子股份有限公司 | Image compressed sensing coding and normalization method based on block vector inner product |
| CN111882489A (en) * | 2020-05-15 | 2020-11-03 | 东北石油大学 | Super-resolution graph recovery method for simultaneously enhancing underwater images |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103037212A (en) * | 2011-10-08 | 2013-04-10 | 太原科技大学 | Adaptive block compressing sensing image coding method based on visual perception |
| CN103886050A (en) * | 2014-03-12 | 2014-06-25 | 佛山科学技术学院 | Image feature storing method, image searching method and device based on compressive sensing |
| CN106056638A (en) * | 2016-05-18 | 2016-10-26 | 信阳师范学院 | Low-complexity compressive sensing image coding method adapted to texture contrast |
-
2017
- 2017-12-29 CN CN201711473464.4A patent/CN108093264B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103037212A (en) * | 2011-10-08 | 2013-04-10 | 太原科技大学 | Adaptive block compressing sensing image coding method based on visual perception |
| CN103886050A (en) * | 2014-03-12 | 2014-06-25 | 佛山科学技术学院 | Image feature storing method, image searching method and device based on compressive sensing |
| CN106056638A (en) * | 2016-05-18 | 2016-10-26 | 信阳师范学院 | Low-complexity compressive sensing image coding method adapted to texture contrast |
Non-Patent Citations (1)
| Title |
|---|
| 刘彦彤: "基于压缩感知的岩心图像压缩与重构技术研究", 《东北石油大学硕士研究生学位论文》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109559357A (en) * | 2018-09-17 | 2019-04-02 | 衡阳师范学院 | A kind of tile compression sensing reconstructing method based on wavelet packet threshold value |
| CN110175965A (en) * | 2019-05-30 | 2019-08-27 | 齐齐哈尔大学 | Based on splits' positions cognitive method that is adaptively sampled and smoothly projecting |
| CN110175965B (en) * | 2019-05-30 | 2020-12-18 | 齐齐哈尔大学 | Block compressed sensing method based on self-adaptive sampling and smooth projection |
| CN110677655A (en) * | 2019-06-21 | 2020-01-10 | 杭州海康威视数字技术股份有限公司 | Encoding and decoding method, device and storage medium |
| CN111193925A (en) * | 2019-12-25 | 2020-05-22 | 杭州中威电子股份有限公司 | Image compressed sensing coding and normalization method based on block vector inner product |
| CN111193925B (en) * | 2019-12-25 | 2021-12-07 | 杭州中威电子股份有限公司 | Image compressed sensing coding and normalization method based on block vector inner product |
| CN111882489A (en) * | 2020-05-15 | 2020-11-03 | 东北石油大学 | Super-resolution graph recovery method for simultaneously enhancing underwater images |
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