32results about How to "Increased PSNR value" patented technology

The invention discloses a two-channel convolutional neural network-based single image super-resolution calculation method. The method comprises the steps of (1) performing fuzzy degradation processing on a known high-resolution image to obtain a low-resolution image with the same size; (2) decomposing the obtained low-resolution image after the fuzzy processing in the step (1) into a texture part and a smooth structure part of the low-resolution image, and obtaining a texture part and a smooth structure part of the high-resolution image; (3) combining the low-resolution texture part obtained in the step (2) and an original low-resolution image to obtain a two-channel input, and obtaining an output of the high-resolution texture part; (4) combining the obtained output of the high-resolution texture part in the step (3) and the original low-resolution image to obtain a final image super-resolution reconstruction result, thereby finishing super-resolution reconstruction; and (5) calculating a difference value between the high-resolution texture parts obtained in the steps (4) and (2) to obtain texture part loss, and minimizing a sum of the texture loss and image loss to optimize network structure parameters.

The invention relates to a coding method and a decoding method of a spatial domain enhancement layer with low code rate based on an interested area. The coding method is as follows: the information of the interested area is loaded from a basic layer; original input images of an enhancement layer are cut according to the information of the interested area; and the interested area obtained after cutting is coded. The invention realizes the function of coding the spatial domain enhancement layer with low code rate based on the interested area, greatly decreases a transmitting bit number after the coding of the spatial domain enhancement layer while the quality of the video information in the spatial domain enhancement layer is ensured not to be changed, and is especially suitable for the transmitting requirement of security protection monitoring.

The invention discloses a non-local mean filtering method based on direction field estimation. The non-local mean filtering method comprises the following steps of receiving a discrete noise fingerprint image, establishing a direction field estimation model of a pixel block in the discrete noise fingerprint image, and performing non-local mean filtering on the discrete noise fingerprint image based on the direction field estimation model to obtain a final denoising fingerprint image. According to the non-local mean filtering method, the problem of poor inhibition performance and poor robustness on noise in the conventional method is solved, contrast among grains in the fingerprint image is enhanced, and characteristic information in the fingerprint image is protected from damage.

The present invention provides an efficient information hiding method of introducing auxiliary pixels. The hiding capacity is irrelevant with the types of the original data and may be the image data or other data, but the hiding quantity is always fixed, so that the efficient information hiding method has the large hiding capacity and the information safety, and is easy to realize by the hardware and software. The method realizes the joint processing of the secret information and the secret keys by introducing the auxiliary pixels and optimizing the data, and a reception terminal can completely recover the secret information and recover the original data with the high quality. The method can hide the information in each pixel of the original image data, enables the hiding efficiency to be improved, breaks through the limit of the conventional method of hiding in a pixel pair (two pixels), enables any image to reach or exceed the 1 / 2 hiding capacity, when the 1 / 2 hiding capacity is realized, enables the PSNR of a carrier image to exceed 41 dB, and is better than the typical hiding methods, for example the least significant bit (LSB) and fully exploiting modification direction (FEMD) hiding methods.

The invention discloses a reversible information hiding method based on block prediction and difference histogram translation, which belongs to the field of digital watermarking and information hiding. The method of the invention first divides the image into 4*4 image blocks, and then performs three-neighborhood Blocking, every four pixels as a group, select a pixel and its surrounding three pixels, use the surrounding three pixels to generate a predicted value, use the predicted value to compare with the selected pixel, generate a difference, and the difference is carried down Rounding to an integer, four difference values ​​are generated in one image block, and then a difference value histogram is generated. The invention improves the embedding capacity of the watermark, simplifies the algorithm steps on the premise of maintaining the visual effect of the image, and only needs to be divided into blocks before embedding and extracting the watermark; the method can not only realize the reversible watermark, but also make the watermark invisible Better, the experiment is simple and easy, the PSNR of the image after embedding the watermark is high, the visual effect of the image is good, and it has practical value.

The invention relates to a method for rate control of video coding, which includes: it allocates the surplus or overruns bit to the follow-up GOP in the controlling method for the GOP layer rate. It sets the quantitative parameters in accordance with the image complexity and pre-bit-rate allocation of I frame to make the use of resources more rationally and effectively in frame-rate controlling method. It controls the quantitative parameters of the current P frames to contain the serious downward trend of P Frame encoding quality due to lack of residual rate when GOP P frame coding rapidly decline.

The invention discloses a partitioning compressed sensing method based on adaptive sampling and smooth projection, and relates to a partitioning compressed sensing method. The objective of the invention is to solve the problems of low quality of reconstructed images and high algorithm complexity in the prior art. The method comprises the following steps: 1, setting an original image, and calculating an adaptive sampling rate of an original image subblock; 2, performing Wiener filtering on the obtained original image sub-block with the known adaptive sampling rate to obtain a reconstructed image subjected to the Wiener filtering process; projecting the jth image sub-block in the reconstructed image to obtain an image sub-block; performing direction transformation to obtain transformed imagesub-blocks, and performing smooth projection to obtain smooth projection image sub-blocks; 3, carrying out image reconstruction according to the smooth projection image sub-blocks, judging whether the obtained reconstructed image meets the requirement for the peak signal to noise ratio of the reconstructed image or not, and if the obtained reconstructed image meets the requirement, obtaining thereconstructed image; otherwise, repeatedly executing the second step and the third step until a reconstructed image meeting requirements is obtained. The method is applied to the field of image reconstruction.

The invention discloses a turbulent image denoising method, comprising the following steps: performing single-layer two-dimensional discrete wavelet transform on the noisy turbulent image; extracting high-frequency coefficients and performing fast discrete Curvelet transform on the noisy turbulent image; The threshold T is estimated by the Yeesian criterion, and the adaptive selection method of the threshold is improved to obtain the optimal threshold and obtain a denoised turbulent image. The present invention provides a turbulent image denoising method, which can well protect image detail information, suppress boundary artifacts, and significantly improve visual effects. At the same time, the invention achieves higher peak signal-to-noise ratio and lower mean square error, and effectively removes the noise of the turbulence degraded image.