6 results about "Block effect" patented technology

The application provides an adaptive atomic force microscope (AFM) imaging method based on block compressed sensing, and comprises the following steps: step S1: a plurality of sub-blocks with the same size are divided on the surface of an AFM sample by using an overlapping block mode; step S2: each sub-block is pre-scanned, a row direction is taken as a fast scanning direction, a lower left corner is taken as a starting point of pre-scanning, and a pre-scanning sampling rate is calculated; step S3: a BP neural network is used to train a sample database, a relationship between a characteristic parameter and a required minimum sampling rate is obtained, and a suitable total sampling rate of each sub-block is adaptively obtained; step S4: a required number of sampling points are randomly selected on the surface of a current sub-block, and adaptive scanning is performed by using a continuous random scanning mode; and step S5: after all sampling points of a sub-block are collected, TVAL3 reconstruction algorithm is combined to perform reconstruction, and finally, all reconstructed sub-blocks are combined together; and the block effect is eliminated by using the overlapping block mode, so that each sample can obtain high and uniform imaging quality.

The application provides a polarization and visible light image fusion texture extraction method and device. A visible light reflection component is first extracted to screen a reliable linear polarization degree image texture and generate a preliminary fusion texture image. Then, a deep learning network model is trained by taking the preliminary fusion texture as a supervised true value, combining a reasonable data enhancement method and a scientific training strategy, so as to eliminate residual block effects and noises in artificial textures. Finally, a fusion texture with high information quantity, high precision, low noise and low block effect is output. In this way, the complementary characteristics of the polarization and visible light images are fully utilized, the texture extraction result is clear and free of artifacts, and the method is suitable for image fusion, edge detection and other real complex scene image detail enhancement and quality optimization.

The application provides a polarization and visible light image fusion texture extraction method and device. A visible light reflection component is first extracted to screen a reliable linear polarization degree image texture and generate a preliminary fusion texture image. Then, a deep learning network model is trained by taking the preliminary fusion texture as a supervised true value, combining a reasonable data enhancement method and a scientific training strategy, so as to eliminate residual block effects and noises in artificial textures. Finally, a fusion texture with high information quantity, high precision, low noise and low block effect is output. In this way, the complementary characteristics of the polarization and visible light images are fully utilized, the texture extraction result is clear and free of artifacts, and the method is suitable for image fusion, edge detection and other real complex scene image detail enhancement and quality optimization.

Disclosed in the present application are an image processing method and apparatus, an electronic device, and a computer readable storage medium. The method comprises: obtaining a target video frame and coding information of the target video frame, the coding information comprising a macro block position of a macro block in the target video frame; on the basis of the macro block position, determining a boundary position of the boundary of the macro block in the target video frame; and, on the basis of the boundary position, determining a block effect area from the target video frame, the block effect area being an area in which a block effect occurs.

The application discloses a blocking effect detection method and device, and relates to the technical field of image processing. A specific embodiment of the method comprises the following steps: in response to determining that a first coding block to be coded in a current frame is a non-boundary coding block, obtaining a quantization parameter of a second coding block and at least one coding block around the second coding block; determining a first target parameter based on the quantization parameter of the second coding block and the at least one coding block around the second coding block; and in response to determining that the first target parameter meets a first preset condition, determining that the first coding block has a blocking effect after coding. The embodiment effectively improves the detection efficiency of the blocking effect.

The present invention proposes a video quality evaluation method based on minimum perceptible difference and block effect, comprising the following steps: step S1: according to the change of pixel values ​​between adjacent blocks of a video frame and the visibility threshold of the human visual system to different background brightness values, the minimum perceptible difference of a pixel point is calculated; the perceptible difference is determined by whether the change of the video pixel point can be perceived by the human eye; step S2: judging the perceptible coding effect of each pixel point by the calculated minimum perceptible difference, and applying the judgment result to the detection of the block effect; step S3: detecting and extracting the significant area of ​​the video by using the frame difference method, and taking the significant area as a reference sample for evaluating the video quality; the present invention can realize real-time and accurate objective quality evaluation of the video.