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To protect edge and texture information, when removing salt‐and‐pepper (SP) noise in grayscale images, a support vector machine (SVM) denoising method is employed. First, a mapping relation between the neighborhood signal pixels and the central pixel is designed. The size of the neighborhood is a 5 × 5 region, with a signal pixel in the center. In this region, a 25‐dimensional input sample is constructed using the correlation between the neighborhood pixels and the eight‐direction fractional integral operators. The center signal pixel acts as the corresponding output sample to provide a training sample. Then, the SVM is trained with all training samples, and the SVM denoising model is obtained. Next, the center pixel value is estimated using the SVM denoising model in every 5 × 5 region with a noise pixel in the center. Finally, the noise pixel values are replaced with the estimated values of the SVM. The experiments demonstrate that the best denoising effect is obtained when the fractional integral order is in the range of 1.8 ± 0.1. The proposed method produces a visually pleasing denoised image and obtains superior image quality assessment indicators. Our method has significant advantages compared with state‐of‐the‐art denoisers when a low level of noise is present.

Inferring all missing high-frequency details by using low-resolution image is the key to super-resolution reconstruction of a single image. In order to extract the feature information in lowresolution images fully and maximize the deduction of high-frequency details, we introduce a multi-scale cross merge (MSCM) network based on residual fusion. The MSCM uses feature extraction module with different-sized convolutional kernels to extract multiple features from the low resolution input image and send them into a nonlinear mapping module after concatenate them together. The nonlinear mapping module consists of five cross-merge modules, each of them formed by cascading three residual dual-branch merged structures. This structure can promote information integration of different branches. Dense connection and residual connection are integrated into the nonlinear mapping module to improve the transmission of information and gradient. The nonlinear mapping module is responsible for extracting highfrequency features and sending them to reconstruction module, which combines an improved sub-pixel upsampling layer with external residual and global residual to generate a high resolution image. Simulation experiments demonstrate that our MSCM network has the ability of achieving single-image superresolution reconstruction, and offers objective and subjective quality improvement compared to mainstream methods and other state-of-the-art reconstruction methods.

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