4 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.

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.